All catalogue courses
Undergraduate Courses
First order differential equations.1. Separable equations.2. Exact equations. Integrating factors.3. Homogeneous equations.4. Linear equations. Equation Bernulli.5. The existence theoremSecond order equations.1. Reduction of order.2. Fundamental solutions of the homogeneous equations3. Linear independence. Liouville formula. Wronskian.4. Homogeneous equations with constant coefficients.5. The nonhomogeneous problem.6. The method of undetermined coefficients.7. The method of variation of parameters. 8. Euler equation.9. Series solutions of second order linear equatHigher order linear equations.1. The Laplace transform2. Definition of the Laplace transform3. Solution of differential equations by method of Laplase transform.4. Step functions.5. The convolution integral.Systems of first order equations.1. Solution of linear systems by elimination.2. Linear homogeneous systems with constant coefficients.3. The matrix method. Eigenvalues and eigenvectors.4. Nonhomogeneous linear systems.
Embedded differentiable manifolds with boundary in Euclidean space. The tangent space, normal, vector fields. Orientable manifolds, the outer normal orientation. Smooth partitions of unity. Differential forms on embedded manifolds, the exterior derivative. Integration of differential forms and the generalized Stokes theorem. Classical formulations (gradient, curl and divergence and the theorems of Green, Stokes and Gauss). Closed and exact forms. Conservative vector fields and existence of potentials. Application to exact ordinary differential equations. Introduction to differential geometry: curvature of curves and surfaces in 3 dimensional space, the Gauss map, the GaussBonnet theorem (time permitting).
2022–23–B
Lecturer: Prof. Michael Levin
Hours:  יום ג 18:00  16:00 in גוטמן [32] חדר 109  יום ה 16:00  14:00 in גולדברגר [28] חדר 107
The course is intended for 3rd year undergraduate as well as M.Sc students both in computer science and mathematics. We will touch main topics in the area of discrete geometry. Some of the topics are motivated by the analysis of algorithms in computational geometry, wireless and sensor networks. Some other beautiful and elegant tools are proved to be powerful in seemingly nonrelated areas such as additive number theory or hard Erdos problems. The course does not require any special background except for basic linear algebra, and a little of probability and combinatorics. During the course many open research problems will be presented.
Detailed Syllabus:
 Fundamental theorems and basic definitions: Convex sets, convex combinations, separation , Helly’s theorem, fractional Helly, Radon’s theorem, Caratheodory’s theorem, centerpoint theorem. Tverberg’s theorem. Planar graphs. Koebe’s Theorem. A geometric proof of the LiptonTarjan separator theorem for planar graphs.
 Geometric graphs: the crossing lemma. Application of crossing lemma to Erdos problems: Incidences between points and lines and points and unit circles. Repeated distance problem, distinct distances problem. Selection lemmas for points inside discs, points inside simplexes. Counting ksets. An application of incidences to additive number theory.
 Coloring and hiting problems for geometric hypergraphs : VCdimension, Transversals and Epsilonnets. Weak epsnets for convex sets. Conflictfree colorings .
 Arrangements : Davenport Schinzel sequences and sub structures in arrangements. Geometric permutations.
 Geometric Ramsey and Turan type theorems: Application of Dilworth theorem, ErdosSzekeres theorem for convex sets, quasiplanar graphs.
 Integral calculus in one variable and its application: the integral, Riemann sums, integrability of bounded functions with countably many discontinuity points (the proofs only for continuous functions and monotone functions), antiderivatives and the Fundamental Theorem of Calculus, change of variables and integrations by parts, partial fractions (without proofs). Applications of integral calculus: computation of areas, volume of the solid of revolution, the length of a smooth curve. Improper integral, and convergence tests for positive functions, application to series.
 Functions of several variables: open, closed, and compact sets, level curves and surfaces, vector valued functions, paths and pathconnectedness.
 Limits and continuity in several variables: arithmetic of limits, Weierstrass theorem, intermediate value theorem.
 Multivariable differential calculus: partial and directional derivatives, differentiability and the tangent plane, the chain rule, the orthogonality of the gradient to the level surfaces, implicit function theorem for a curve in the plane and a surface in the space (without a proof), the Hessian, Taylor approximation of order 2, critical points (classification only in dimension 2), Extremum problem, including Lagrange multipliers and gradient descent.
 Integration in dimension 2: Reimann integral in dimension 2, change of variables and Fubini theorem (without proofs), changing the order of integration, polar coordinates, computation of volumes. If time permits: integration in dimension 3.
2022–23–B
Lecturer: Dr. Dennis Gulko
Hours:  יום ד 10:00  08:00 in גולדברגר [28] חדר 201  יום ה 10:00  08:00 in צוקר, גולדשטייןגורן [72] חדר 124
 The Poisson Process and its applications. Elements of queuing theory. Queuing birthanddeath processes in equilibrium. Nonhomogeneous Poisson Processes.
 Symmetric Brownian Motion. Brownian Motion with drift. FirstPassage times and arcsine laws. Geometric Brownian motion. Application to option pricing by the Arbitrage Theorem (Black and Scholes).
 Discretetime Markov Chains. Classification of states. Random walks in the line, plane and space. Limit theorems. Calculation of limiting proportions of sojourn times and expectations of recurrence times.
The system of the real numbers (without Dedekind cuts). The supremum axiom. Convergent sequences, subsequences, monotonic sequences, upper and lower limits. Series: partial sums, convergent and divergent series, examples, nonnegative series, the root test, the quotient test, general series, Dirichlet, Leibnitz, absolute convergence implies convergence (without a proof). Limits of functions, continuity, the continuity of the elementary functions, extrema in compact intervals. The derivative of a function, Lagrange’s Mean Value Theorem, high order derivatives, L’hospital’s rules, Taylor’s Theorem, error estimates, lots of examples. The Riemann integral: only for piecewise continuous functions (finitely many points of discontinuity). Riemann sums and the definition of the integral, The Fundamental Theorem of Calculus, the existence of primitive functions (antiderivatives). Integration techniques: integration by parts, substitutions, partial fractions (without proofs), improper integrals, applications of integrals, estimation of series with the aid of integrals, Hardy’s symbols O, o and Omega, approximation of momenta and the Stirling formula.
2022–23–B
Lecturer: Dr. Saak Gabriyelyan
 Complex numbers: Cartesian coordinates, polar coordinates. Functions of a complex variable. Basic properties of analytic functions, the exponential function, trigonometric functions. Definition of contour integral. The Cauchy Integral Formula. Residues and poles. Evaluation of impoper real integrals with the use of residues.
 Inner product functional spaces. Orthogonal and orthonormal systems. Generalized Fourier series. Theorem on orthogonal projection. Bessel’s inequality and Parseval’s equality.
 Trigonometric Fourier series. Complex form of Fourier series. Fourier series expansion defined over various intervals. Pointwise and uniform convergence of Fourier series. Completness of trigonometric system and Parseval’s equality. Differentiation and integration of Fourier series.
 The Fourier integral as a limit of Fourier series. The Fourier transform: definition and basic properties. The inverse Fourier transform. The convolution theorem, Parseval’s theorem for the Fourier transform. A relation between Fourier and Laplace transforms. Application of Fourier transform to partial differential equations and image processing.
 Distributions (generalized functions). The Heaviside step function, the impulse deltafunction. Derivative of distribution. Convergence of sequences in the space of distributions. The Fourier transform of distributions.
An introduction to applications of algebra and number theory in the field of cryptography. In particular, the use of elliptic curves in cryptography is studied in great detail.
 Introduction to cryptography and in particular to public key systems, RSA, DiffieHellman, ElGamal.
 Finite filelds, construction of all finite fields, efficient arithmetic in finite fields.
 Elliptic curves, the group law of an elliptic curve, methods for counting the number of points of an elliptic curves over a finite field: Babystep giant step, Schoof’s method.
 Construction of elliptic curves based cryptographic systems.
 Methods for prime decomposition, the elliptic curves method, the quadratic sieve method.
 Safety of public key cryptographic methods.
Number Theory studies the structure of the integers and the natural numbers. In addition to classical topics (prime numbers, congruences, quadratic residues, etc.) there is an emphasis on algorithmic questions and in particular on applications to cryptography.
 Divisibility and prime numbers
 Congruences
 The multiplicative group of $\mathbb{Z}/m$
 Quadratic residues
 Continued fractions
 Algebraic numbers and algebraic integers
 Rings and modules, Polynomial rings in several variables over a field
 Monomial orders and the division algorithm in several variables
 Grobner bases and the Buchberger algorithm, Elimination and equation solving
 Applications of Grobner bases:
 integer programming
 graph coloring
 robotics
 coding theory
 combinatorics and more
 The Hilbert function and the Hilbert series, Speeding up the Buchberger algorithm, The f4 and f5 algorithms
 Introduction to scalars and vectors: operations with scalars and vectors, scalar product, vector product, vector equations, indices, Einstein summation convention, kronecker delta, LeviCivita symbol.
 Introduction to functions: types, continuity, limits, derivatives, integrals, integration methods, taylor series, remainder.
 Functions with scalar input and vector output: curves, tangent, normal, velocity, acceleration, curvature, torsion. FrenetSerret basis and kinematics.
 Functions with vector input and scalar output: extrema, contours, gradient, directional derivative, tangent space.
 Multiple integrals
 Functions with vector input and vector output: conservative fields, rotational fields, divergence, curl, line integral, surface integral, Stokes and Gauss
 Differential equations: damped harmonic oscillator with driving
 Rotations: scalars, vectors, tensors
 Curvilinear coordinates
 An introductory sketch and some motivating examples. Degenerate critical points of functions. Singular (nonsmooth) points of curves.
 Holomorphic functions of several variables. Weierstrass preparation theorem. Local Rings and germs of functions/sets.
 Isolated critical points of holomorphic functions. Unfolding and morsication. Finitely determined function germs.
 Classification of simple singularities. Basic singularity invariants. Plane curve singularities. Decomposition into branches and Puiseux expansion.
 Time permitting we will concentrate on some of the following topics: a. Blowups and resolution of plane curve singularities; b. Basic topological invariants of plane curve singularities (Milnor fibration); c. Versal deformation and the discriminant.
The course covers central ideas and central methods in classical set theory, without the axiomatic development that is required for proving independence results. The course is aimed as 2nd and 3rd year students and will equip its participants with a broad variety of set theoretic proof techniques that can be used in different branches of modern mathematics.
Sylabus
 The notion of cardinality. Computation of cardinalities of various known sets.
 Sets of real numbers. The CantorBendixsohn derivative. The structure of closed subsets of Euclidean spaces.
 What is Cantor’s Continuum Hypothesis.
 Ordinals. Which ordinals are orderembeddable into the real line. Existence theorems ordinals. Hartogs’ theorem.
 Transfinite recursion. Applications.
 Various formulations of Zermelo’s axiom of Choice. Applications in algebra and geometry.
 Cardinals as initial ordinals. Hausdorff’s cofinality function. Regular and singular cardinals.
 Hausdorff’s formula. Konig’s lemma. Constraints of cardinal arithmetic.
 Ideal and filters. Ultrafilters and their applications.
 The filter of closed and unbounded subsets of a regular uncountable cardinal. Fodor’s pressing down lemma and applications in combinatorics.
 Partition calculus of infinite cardinals and ordinals. Ramsey’s theorem. The ErdosRado theorem. DushnikMiller theorem. Applications.
 Combinatorics of singular cardinals. Silver’s theorem.
 Negative partition theorems. Todorcevic’s theorem.
 Other topics
Bibliography.
 Winfried Just and Martin Wese. Discovering modern set theory I, II. Graduate Studies in Mathematics, vol. 8, The AMS, 1996.
 Azriel Levy. Basic Set Theory. Dover, 2002.
 Ralf Schindler. Set Theory. Springer 2014.
The course will present Game Theory mostly from a mathematical point of view. Topics to be covered:
 Combinatorial games.
 Twoperson zerosum games.
 Linear programming.
 Generalsum games.
 Equilibrium points.
 Randomturn games.
 Stable marriages.
 Voting.
Coding Theory investigates errordetection and errorcorrection. Such errors can occur in various communication channels: satellite communication, cellular telephones, CDs and DVDs, barcode reading at the supermarket, etc. A mathematical analysis of the notions of error detection and correction leads to deep combinatorial problems, which can be sometimes solved using techniques ranging from linear algebra and ring theory to algebraic geometry and number theory. These techniques are in fact used in the abovementioned communication technologies.
Topics
 The main problem of Coding Theory
 Bounds on codes
 Finite fields
 Linear codes
 Perfect codes
 Cyclic codes
 Sphere packing
 Asymptotic bounds
Bibliography:
R. Hill, A First Course in Coding Theory, Clarendon Press, Oxford 1986
 Real numbers and real line, elementary functions and graphs, some functions arising in economics. The limit of a function, calculating limits using the limit laws, continuity, the number e.2. The derivative of a function, differential rules, higher derivatives, L’Hospital rules.3. Extreme values of functions, monotonic functions, point of inflection, concavity, curve sketching, applications to economics.4. Indefinite integrals, techniques of integration, definite and improper integrals, areas between curves, applications to economics.5. Functions of several variables, economics examples, partial derivatives, linearization, the chain rile, implicit and homogeneous functions, maximum and minimum, Lagrange multipliers.6. Introduction to linear algebra, matrices, linear systems.
 Classification of linear Partial Differential Equations of order 2, canonical form.
 Fourier series (definition, Fourier theorem, odd and even periodic extensions, derivative, uniform convergence).
 Examples: Heat equation (Dirichlet’s and Newman’s problems), Wave equation (mixed type problem), Potential equation on a rectangle.
 Superposition of solutions, nonhomogeneous equation.
 Infinite and semiinfinite Heat equation: Fourier integral, Green’s function. Duhamel’s principle.
 Infinite and semiinfinite Wave equation: D’Alembert’s solution.
 Potential equation on the disc: Poisson’s formula and solution as series.

Fields: the definition of a field, complex numbers.

Linear equations: elementary operations, row reduction, homogeneous and nonhomogeneous equations, parametrization of solutions.

Vector spaces: examplex, subspaces, linear independence, bases, dimension.

Matrix algebra: matrix addition and multiplication, elementary operations, the inverse matrix, the determinant and Cramer’s law. Linear transformations: examples, kernel and image, matrix representation.
 Introduction.
 Combinatorial objects: representation, identification, symmetry, constructive and analytical enumeration. The natural numbers, integers, rational and irrational numbers. The arithmetic operations, powers and radicals. The basic algebraic formulas including the binomial formula.
 Permutation groups and combinatorial enumeration: Permutation groups. Introduction to Polya enumeration theory. Graph isomorphism problem.
 Finite fields and applications: Finite fields. Coding theory. Incidence structures and block designs.
 Symmetrical graphs: Cayley graphs, strongly regular graphs. $n$dimensional cubes and distance transitive graphs.
 Examples of applications: Design of statistical experiments. Cryptography. Recreational mathematics.
Topological spaces and continuous functions (product topology, quotient topology, metric topology). Connectedness and Compactness. Countabilty Axioms and Separation Axioms (the Urysohn lemma, the Urysohn metrization theorem, Partition of unity). The Tychonoff theorem and the StoneCech compactification. Metrization theorems and paracompactness.
2022–23–B
Lecturer: Prof. Menachem Kojman
Hours:  יום ב 10:00  08:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 209  יום ד 16:00  14:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 3
Part A: Logic and settheory. Propositional calculus, Boolean operations. Truth tables, the truthvalue of a propositional formula (without induction at this stage), logical implication and logical equivalence, tautologies and contradictions, the useful tautologies, distributivity and deMorgan’s Law. Sets: the notion of a set, membership and equality, operations: union, intersection, setdifference and powerset. Ordered pairs and Cartesian products. Equivalence relations, quotient spaces and partitions.Partial orders. Functions, injective and surjective functions, invertibility of a function. The ordered set of natural numbers.The axiom of induction in different forms.
Part B: Finite and infinite sets. The notion of cardinality. Countable sets. Cantor’s theorem on the power set of a set.
Part C: Combinatorics. Basic counting formulas. Binomials. Inclusionexclusion technique. Recursive definition and formulas.
Part D: Graph Theory. Graphs, examples, basic facts, vertex degrees, representing a graph, neighborhood matrices, connected components, Euler graphs, bipartite graphs, matching in bipartite graphs, Hall’s marriage theorem, graph colorings.
2022–23–B
Lecturer: Dr. Stewart Smith
 Basic notions: equations of the first order, general solution, initial value problem, particular solution. Linear equations, separable equations, exact equations, homogeneous equations, integrating factor. The existence and uniqueness theorem (without proof). The Riccatti equations, the Bernoulli equations. Linear systems of the first order differential equations. Solution via the matrix calculus. The second order linear equations. Nonlinear equations and the Wronskian. The Euler equations. Linear equations of the first order. 2. The Laplace transformation, properties of the Laplace transformation, solutions of the linear nonhomogeneous equations via the Laplace transformation, the Heaviside functions, the delta functions.3. The Fourier transformation, properties of the Fourier transformation. Cosine and sine Fourier transformation. Solution of the integral equations via the Fourier transformation.
Prerequisites: 20119531 Linear Algebra
Brief syllabus

Operations over sets, logical notation, relations.

Enumeration of combinatorial objects: integer numbers, functions, main principles of combinatorics.

Elementary combinatorics: ordered and unordered sets and multisets, binomial and multinomial coefficients.

Principle of inclusion and exclusion, Euler function.

Graphs: representation and isomorphism of graphs, valency, paths and cycles.

Recursion and generating functions: recursive definitions, usual and exponential generating functions, linear recurrent relations with constant coefficients.

(Optional) Modular arithmetics: congruences of integer numbers, $\mathbb{Z}_m$, invertible elements in $\mathbb{Z}_m$.
Ordinary differential equations of first order, existence and uniqueness theorems, linear equations of order $n$ and the Wronskian, vector fields and autonomous equations, systems of linear differential equations, nonlinear systems of differential equations and stability near equilibrium
2022–23–B
Lecturer: Prof. Dmitry Kerner
Hours:  יום ב 16:00  14:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 103  יום ה 14:00  12:00 in בנין 90 (מקיף ז') [90] חדר 229
 The real numbers, inequalities in real numbers, the complex numbers, the Cartesian representation, the polar representation, the exponential representation, the Theorem of de Moivre, root computations.
 Systems of linear equations over the real or complex numbers, the solution set and its parametric representation, echelon form and the reduced echelon form of a matrix, backwards substitution, forward substitution and their complexity, the Gauss elimination algorithm and its complexity, the reduction algorithm and its complexity.
 Vector spaces, subspaces of vector spaces, linear combinations of vectors, the span of a set of vectors, linear dependence and linear independence, the dimension of a vector space, row spaces and column spaces of matrices, the rank of a matrix.
 Linear mappings between vector spaces, invertible mappings and isomorphisms, the matrix representation of finite dimensional linear mappings, inversion of a square matrix, composition of mappings, multiplication of matrices, the algebra of matrices, the kernel and the image of a linear mapping and the computation of bases, changing of a basis, the dimension theorem for linear mappings.
 Inner product spaces, orthogonality, the norm of a vector, orthonormal sets of vectors, the CauchySchwarz inequality, the orthogonal complement of a subspace, orthogonal sequences of vectors, the GramSchmidt algorithm, orthogonal transformations and orthogonal matrices.
 The determinant of a square matrix, minors and cofactors, Laplace expansions of the determinant, the adjoint matrix and Laplace theorem, conjugation of a square matrix, similarity transformations and their invariants (the determinant and the trace).
 Eigenvalues, eigenvectors, eigenspaces, diagonalization and similarity, the characteristic polynomial, the algebraic and the geometric multiplicities of an eigenvalue, the spectral theorem for Hermitian matrices.
2022–23–B
Lecturer: Prof. Mikhail Muzychuk
 Rings and ideals (revisited and expanded).
 Modules, exact sequences, tensor products.
 Noetherian rings and modules over them.
 Hilbert’s basis theorem.
 Finitely generated modules over PID.
 Hilbert’s Nullstellensatz.
 Affine varieties.
 Prime ideals and localization. Primary decomposition.
 Discrete valuation rings.

Fields: the definition of a field, complex numbers.

Linear equations: elementary operations, row reduction, homogeneous and nonhomogeneous equations, parametrization of solutions.

Vector spaces: examplex, subspaces, linear independence, bases, dimension.

Matrix algebra: matrix addition and multiplication, elementary operations, the inverse matrix, the determinant and Cramer’s law. Linear transformations: examples, kernel and image, matrix representation.
2022–23–B
Lecturer: Dr. Yaar Solomon
Fields. Fields of rational, real and complex numbers. Finite fields. Calculations with complex numbers. Systems of linear equations. Gauss elimination method. Matrices. Canonical form of a matrix. Vector spaces . Homogeneous and non homogeneous systems. Vector spaces. Vector spaces. Vector subspace generated by a system of vectors. Vector subspace of solutions of a system of linear homogeneous equations. Linear dependence. Mutual disposition of subspaces. Basis and dimension of a vector space. Rank of a matrix. Intersection and sum of vector subspaces. Matrices and determinants. Operations with matrices. Invertible matrices. Change of a basis. Determinants. Polynomials over fields. Divisibility. Decomposition into prime polynomials over R and over C. Linear transformations and matrices. Linear transformations and matrices. Kernel and image. Linear operators and matrices. Algebra of linear operators. Invertible linear operators. Eigenvectors and eigenvalues of matrices and linear operators. Diagonalization of matrices and linear operators. Scalar multiplication. Orthogonalization process of GramShmidt. Orthogonal diagonalization of symmetric matrices.
The aim of the course is to learn basis of Calculus of functions of two and more variables. It includes: a) short study of vector algebra and analytic geometry in plane and space; b) differential calculus of two and more variables and its applications to local and global extremum problems, directional derivatives, Teylor’s formula, etc.;c) Integral calculus (line, double and triple integrals) and its applications; d) vector field theory and in particular its applications for studying potential vector fields.
2022–23–B
Lecturer: Dr. Irena Lerman
 Geometry of Curves. Parametrizations, arc length, curvature, torsion, Frenet equations, global properties of curves in the plane.
 Extrinsic Geometry of Surfaces. Parametrizations, tangent plane, differentials, first and second fundamental forms, curves in surfaces, normal and geodesic curvature of curves.
 Differential equations without coordinates. Vector and line fields and flows, frame fields, Frobenius theorem. Geometry of fixed point and singular points in ODEs.
 Intrinsic and Extrinsic Geometry of Surfaces. Frames and frame fields, covariant derivatives and connections, Riemannian metric, Gaussian curvature, Fundamental Forms and the equations of Gauss and CodazziMainardi.
 Geometry of geodesics. Exponential map, geodesic polar coordinates, properties of geodesics, Jacobi fields, convex neighborhoods.
 Global results about surfaces. The GaussBonnet Theorem, HopfRinow theorem, HopfPoincaret theorem.
1) The system of the real numbers: the natural numbers, well order, the inegers, the rational numbers, arithmetical operations and the field axioms, the order on the rational numbers, the Archimedian axiom, non completeness of the rationals, irrational numbers and the completeness of the reals, bounded sets, upper and lower bounds, maximum and minimum, supremum and infimum, rational and irrational powers, the basic inequalities (Bernoulli, CauchySchwarz, the mean values, Holder, Minkowskii), the rational roots of a polynomial equation over the rational numbers.2) Sequences and their limits, the arithmetic of limits, divergence and tending to infinity, inequalities between sequences and between their limits, the Sandwitch Theorem, monotonic sequences, recursive sequences, Cantor’s Lemma, sub sequences, the Theorem of BolzanoWeierstrass, the exponent, Cauchy’s criterion for the convergence of a sequence, upper limit, lower limit.3) Functions of a single variable, arithmetic operations on functions, monotone functions, the elementary functions.4) The limit of a function, the sequential definition and non sequential definition, the arithmetic of limits, one sided limits, Cauchy’s criterion, bounded functions, the order of magnitude of a function (big Oh and small Oh).5) Continuous functions, classification of discontinuities, the mean value theorem and its applications, continuity and being monotone.6) The derivative of a function, the graph of a function and the tangent line to the graph, the slope of the tangent line, the velocity, differentiability and continuity, the arithmetic of the differential operator in particular the Liebnitz rule, composition of functions, the chain rule, higher order derivatives, a theorem of Fermat, Roll’s Theorem, the maen value theorem of Lagrange, the mean value theorem of Cauchy, L’hospital rules, Taylor’s Theorem.7) Local and global maximum and minimum, inflection points, convex and concave functions, asymptotic lines, sketching
 The Fourier transform: convolutions, the inversion formula, Plancherel’s theorem, Hermite functions, tempered distributions. The Poisson summation formula. The Fourier transform in R^n.
 The Laplace transform. Connections with convolutions and the Fourier transform. Laguerre polynomials. Applications to ODE’s. Uniqueness, Lerch’s theorem.
 Classification of the second order PDE: elliptic, hyperbolic and parabolic equations, examples of Laplace, Wave and Heat equations.
 Elliptic equations: Laplace and Poisson equations, Dirichlet and Neumann boundary value problems, Poisson kernel, Green’s functions, properties of harmonic functions, Maximum principle
 Analytical methods for resolving partial differential equations: SturmLiouville problem and the method of separation of variables for bounded domains, applications for Laplace, Wave and Heat equations including nonhomogenous problems. Applications of Fourier and Laplace transforms for resolving problems in unbounded domains.
Bibliography
 Stein E. and Shakarchi R., Fourier analysis, Princeton University Press, 2003.
 Korner T.W., Fourier analysis, Cambridge University Press, 1988.
 Katznelson Y., An Introduction to Harmonic Analysis, Dover publications. 4. John, Partial differential equations, Reprint of the fourth edition. Applied Mathematical Sciences, 1. SpringerVerlag, New York, 1991.
 Evans Lawrence C. Partial Differential Equations, Second Edition.
 Gilbarg D.; Trudinger N. S. Elliptic partial differential equations of second order, Reprint of the 1998 edition. Classics in Mathematics. SpringerVer lag, Berlin, 2001.
 Zauderer E. Partial differential equations of applied mathematics, Second edition. Pure and Applied Mathematics (New York). A WileyInterscience Publication. John Wiley & Sons, Inc., New York, 1989. xvi+891 pp. ISBN: 0471612987.
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The Lab will be based on a LINUX platform, and use the C programming language. The emphasis is on lowlevel programming. Goals of this lab are to introduce issues in low level programming, as well as techniques on how to learn needed information on demand
 Affine and projective spaces, affine and projective maps, Segre and Veronese embeddings, Desargues’s Theorem, Pappus’s Theorem, crossratio, projective duality
 Plane curves: rational curves, linear systems of curves, conics and the Butterfly Theorem, Pascal’s Theorem, Chasles’s Theorem, the group structure on a planar cubic, Bezout’s Theorem
 Affine algebraic varieties: Hilbert’s Basis Theorem, Zariski topology, irreducible components, Hilbert’s Nullstellensatz, the correspondence between the ideals and the algebraic sets, morphisms and rational maps between affine algebraic varieties
 Projective varieties: graded rings and homogeneous ideals, the projective correspondence, morphisms, blowups, birational equivalence and rational varieties, Grassmannians
 The basics of dimension theory
 The basics of smoothness
 Cubic surfaces and 27 lines. If time permits, other topics will be discussed such as abstract algebraic varieties, Chevaley’s Theorem, RiemannRoch Theorem and its applications.
 Variable types: Numbers and strings, basic operations
 Input and output
 Conditionals and loops
 Lists, dictionaries and other data types
 Functions
 Recursions
 Object oriented programming
 Modules
 Introduction to computational complexity
Graphs and subgraphs, trees, connectivity, Euler tours, Hamilton cycles, matching, vertex and edge colorings, planar graphs, introduction to Ramsey theory, directed graphs, probabilistic methods and linear algebra tools in Graph Theory.
2022–23–B
Lecturer: Dr. Yaar Solomon
Hours:  יום ב 12:00  10:00 in גוטמן [32] חדר 309  יום ג 14:00  12:00 in גוטמן [32] חדר 209
 Introduction: Actions of groups on sets. Induced linear actions. Multilinear algebra.
 Representations of groups, direct sum. Irreducible representations, semisimple representations. Schur’s lemma. Irreducible representations of finite abelian groups. Complete reducibility, Machke’s theorem.
 Equivalent representations. Morphisms between representations. The category of representations of a finite group. A description using the group ring. Multilinear algebra of representations: dual representation, tensor product (inner and outer).
 Decomposition of the regular representation into irreducible representations. The number of irreducibles is equal to the number of conjugacy classes. Matrix coefficients, characters, orthogonality.
 Harmonic analysis: Fourier transform on finite groups and the noncommutative Fourier transform.
 Frobenius divisibility and Burnside $p^aq^b$ theorem.
 Constructions of representations: induced representations. Frobenius reciprocity. The character of induced representation. Mackey’s formula. Mackey’s method for representations of semidirect products.
 Induction functor: as adjoint to restrictions, relation to tensor product. Restriction problems, multiplicity problems, Gelfand pairs and relative representation theory.
 Examples of representations of specific groups: $SL_2$ over finite fields, Icosahedron group, Symmetric groups.
 Artin and Brauer Theorems on monomial representations
 Partially ordered sets. Chains and antichains. Examples. Erdos–Szekeres’ theorem or a similar theorem. The construction of a poset over the quotient space of a quasiordered set.
 Comparison of sets. The definition of cardinality as as an equivalence class over equinumerousity. The CantorBernstein theorem. Cantor’s theorem on the cardinality of the powerset.
 Countable sets. The square of the natural numbers. Finite sequences over a countable set. Construction of the ordered set of rational numbers. Uniqueness of the rational ordering.
 Ramsey’s theorem. Applications.
 The construction of the ordered real line as a quotient over Cauchy sequences of rationals.
 Konig’s lemma on countably infinite trees with finite levels. Applications. A countable graph is kcolorable iff every finite subgraph of it is kcolorable.
 Well ordering. Isomorphisms between wellordered sets. The axiom of choice formulated as the wellordering principle. Example. Applications. An arbitrary graph is k–colorable iff every finite subgraph is kcolorable.
 Zorn’s lemma. Applications. Existence of a basis in a vector space. Existence of a spanning tree in an arbitrary graph.
 Discussion of the axioms of set theory and the need for them. Russel’s paradox. Ordinals.
 Transfinite induction and recursion. Applications. Construction of a subset of the plane with exactly 2 point in every line.
 Infinite cardinals as initial ordinals. Basic cardinal arithmetic. Cardinalities of well known sets. Continuous real functions, all real runctions, the automorphisms of the real field (with and without order).
2022–23–B
Lecturer: Prof. Assaf Hasson
Hours:  יום א 14:00  12:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 7  יום ג 12:00  10:00 in ביה"ס אילנות [97] חדר 202
סילבוס אינו מוגדר בתקופה המתבקשת
The goal of the workshop is to accompany first year mathematics majors, and to improve their skills in writing formal proofs. In the course of the workshop, the students will work in small groups on writing proofs, with an emphasis on topics related to the foundational first year courses.
Sets. Set operations and the laws of set theory. Power set. Cartesian product of sets.The rules of sum and product. Permutations, combination, distributions. The Binomial Theorem. The wellordering principle: mathematical induction. The principle of inclusion and exclusion. The pigeonhole principle. Recurrence relations. Generating functions.Relations and functions. Properties of relations. Equivalence relations and their properties. Partial order. Functions and their properties. Injective, surjective functions. Function composition and inverse functions.Graph, subgraph, complements. Graph isomorphism. Euler`s formula. Planar graph. Euler trails and circuits. Trees.Propositional logic. Syntax of propositional logic. Logical equivalence. The laws of logic. Logical implication. Equivalence and disjunctive normal form. Predicate logic. Syntax of predicate logic. Models. Equivalence of formulas. Normal form.Algebraic structures. Rings, groups, fields. The integer modulo n. Boolean algebra and its structure.
Open, closed and compact sets in Euclidean space. Matrix norms and equivalence of norms. Limits and continuity in several variables. Curves and path connectedness. Partial and directional derivatives, the gradient and differentiability. The implicit, open and inverse function theorems. Largange multipliers. Optimization: the Hessian matrix and critical points. Multivariable Riemann integration: Fubini’s theorem and the change of variables formula.
Algebras and sigmaalgebras of subsets, the extension theorem and construction of Lebesgue’s measure on the line, general measure spaces, measurable functions and their distribution functions, integration theory, convergence theorems (Egorov’s, relations between convergence in measure and a.e. convergence), the spaces $L_1$ and $L_2$ and their completeness, signed measures, the RadonNikodym theorem, measures in product spaces and Fubini’s theorem.
Complex numbers.Systems of linear equations. Solving linear systems: row reduction and echelon forms. Homogenous and inhomogenous systems.Rank of matrix.Vector spaces. Linearly independent and linearly dependent sets of vectors. Linear combinations of vectors.Inner (dot) product, length, and orthogonality. The Gram  Schmidt process.Matrices: vector space of matrices, linear matrix operations, matrix multiplication, inverse matrix. An algorithm for finding inverse matrix by means of elementary row operations.Rank of matrix and its invertibility. Solving systems of linear equations by means of inverse matrix.Determinants. Condition detA=0 and its meaning. Tranposed matrix.Eigenvectors and eigenvalues. The characteristic polynomial and characteristic equation. Finding of eigenvectors and eigenvalues.Diagonalization and diagonalizable matrices. Symmetric matrices.
 Preliminaries: floating point arithmetic, roundoff errors and stability. Matrix norms and the condition number of a matrix.
 Introduction to numerical solutions for ODE’s:initial value problems, Euler’s method, introduction to multistep methods. Boundary value problems.
 Numerical solution of linear equations: Gauss elimination with pivoting, LU decomposition. Iterative techniques: Jacobi, GaussSeidel, conjugate gradient. Least squares approximation.
 Numerical methods for finding eigenvalues: Gershgorin circles. The power method. Stability considerations in GramSchmidt: Hausholder reflections and Givens rotations. Hessenberg and tridiagonal forms. QR decomposition and the QR algorithm.
2022–23–B
Lecturer: Prof. Yair Glasner
Hours:  יום א 14:00  12:00 in גולדברגר [28] חדר 106  יום ה 12:00  10:00 in מנדל [14] חדר 115
Analytic Geometry: planes and lines, quadric surfaces, cylinders.Vector functions: derivatives and integrals.Partial derivatives: functions of two or more arguments, chain rules, gradient, directional derivatives, tangent planes, higher order derivatives, linear approximation, differential of the first and higher order, maxima, minima and saddle points, Lagrange multipliers.Multiple integrals: double integrals, area, changing to polar coordinates, triple integrals in rectangular coordinates, physical applications.Vector analysis: vector and scalar fields, surface integrals, line integrals and work, Green’s theorem, the divergence theorem, Stokes’s theorem.Infinite series: tests for convergence of series with nonnegative terms, absolute convergence, Alternating series, conditional convergence, arbitrary series.Power series: power series for functions, Taylor’s theorem with remainder: sine, cosine and e , logarithm, arctangent, convergence of power series, integration, differentiation.
סילבוס אינו מוגדר בתקופה המתבקשת
In the 1980s A. Grothendieck suggest a project for developing a tame topology that will not suffer from the many counterexamples and pathologies known in classical topology. Nowadays many view the notion of ominimality as successful fulfillment of this program: in ominimal fields all (unary) functions are piecewise differentiable (and therefore infinitely differentiable at almost every point); unary functions are piecewise monotone, connectedness is the same as path connectedness and the axiom of choice holds for definable sets. In the ominimal setting most of the classical differential calculus can be developed, and so are large portion of the theory of Lie groups, algebraic topology and much more. Ominimality plays a key role in real geometry and in recent years had a crucial role in important breakthroughs in Diophantine geometry and in Hodge theory.
In the course we will define ominimality and develop its basic theory. We will show that real closed fields are ominimal and discuss – time permitting – some applications.
 Second order linear equations with two variables: classification of the equations in the case of constant and variable coefficients, characteristics, canonical forms.
 SturmLiouville theory.
 String or wave equation. Initial and boundary value conditions (fixed and free boundary conditions). The d’Alembert method for an infinitely long string. Characteristics. Wave problems for halfinfinite and finite strings. A solution of a problem for a finite string with fixed and free boundary conditions by the method of separation of variables. The uniqueness proof by the energy method. Wellposedness of the vibrating string problem.
 Laplace and Poisson equations. Maximum principle. Wellposedness of the Dirichlet problem. Laplace equation in a rectangle. Laplace equation in a circle and Poisson formula. An illposed problem  the Cauchy problem. Uniqueness of a solution of the Dirichlet problem. Green formula in the plane and its application to Neumann problems.
 Heat equation. The method of separation of variables for the onedimensional heat equation. Maximum principle. Uniqueness for the onedimensional heat equation. The Cauchy problem for heat equations. Green?s function in one dimension. If time permits: Green?s function in the two dimensional case.
 Nonhomogeneous heat equations, Poisson equations in a circle and nonhomogeneous wave equations.
 If time permits: free vibrations in circular membranes. Bessel equations.
This course is meant to discuss problems and provide examples in the following topics. Close coordination with the parallel course Geometric Calculus 1 is recommended.
 Topology of $\mathbb{R}^n$: open, closed, compact and connected sets.
 Continuity and differentiability of functions from $\mathbb{R}^m$ to $\mathbb{R}^n$, including the basic geometric properties of directional derivatives and the gradient. Curves in $\mathbb{R}^n$.
 Implicit and inverse function theorems
 Taylor’s theorem for multivariable functions and the Hessian
 Extrema for multivariable functions, with and without constraints
 Fubini’s theorem and the change of variables formula
Topics: 1. Limits and Continuity of functions, applications 2. Differentiability of functions, applications 3. Differentiation techniques 4. Differentiation of Implicit functions, applications 5. Investigation of functions. 6. Multivariable functions, Partial derivatives, applications 7. The Definite Integral 8. The Indefinite Integral 9. Applications of Integrals 10. Integration techniques 11. Taylor polynomials 12. Simple Differential Equations.
 Analytic geometry in space. Vector algebra in R3. Scalar, cross and triple product and their geometric meaning. Lines, planes and quadric surfaces in space including the standard equations for cones, ellipsoids, paraboloids and hyperboloids.
 Functions of several variables.Graphs and level curves and surfaces. Limits and continuity. Properties of the continuous functions on a closed bounded domain. Partial derivatives. The plane tangent to graph of the function. Differentiability, the total differential and the linear approximation. Differentiability implies continuity. The chain rule. The gradient vector and the directional derivative. Tangent plane and the normal line to a surface at a point. 201.1.9761
 Maxima and minima for functions of several variables. Higherorder partial derivatives and differentials. Taylor’s formula. Local extrema and saddle points. Necessary conditions for local maxima and minima of a differentiable function. Sufficient conditions for local maxima and minima via the Hessian. Global extrema in closed bounded sets. Lagrange Multipliers.
 Double integrals . Double integrals on rectangles. Connection with the volume. Properties and evaluation of double integrals in nonrectangular domains. Iterated integrals and change of order of integration. Change of variables formula for the double integral and the Jacobian. Double integrals in polar coordinates. Applications of the change of variables formula to the computation of area.
Ordinary Differential EquationsBasic concepts: ordinary differential equations, differential equations of the first order, general solution, initial value problems, partial solutions. Linear differential equations with separable variables, exact equations, integration factor, homogeneous equations. Existence and Uniqueness theorem (without proof). System of differential equation of first order, solution by matrixes. Linear differential equations of second order, non homogeneous equations, Wronskian. Linear differential equations of nth order.Integral TransformsLaplace transform, properties of the Laplace transform. Convolution of two functions and convolution theorem. Heavyside (unit step) function, ?function (Dirac), particularly continuous functions, their Laplace transform. Solution of nonhomogeneous differential equations by Laplace transform.Fourier transform, properties of the Fourier transform. Convolution of two functions and convolution theorem. Cosines and Sine Fourier transform. Solution of integral equations by Fourier transform..
2022–23–B
Lecturer: Dr. Irena Lerman
 Complex valuedfunctions and the complex exponential. Fourier coefficients of piecewise continuous periodic functions. Basic operations and their effects on Fourier coefficients: translation, modulation, convolutions, derivatives.
 Uniform convergence: Cesaro means, the Dirichlet and Fejer kernels, Fejer’s theorem. The Weierstrass approximation theorem for trigonometric polynomials and for polynomials. Uniqueness of Fourier coefficients. The RiemannLebesgue lemma. Hausdorff’s moment problem. Convergence of partial sums and Fourier series for $C^2$functions.
 Pointwise convergence: Dini’s criterion. Convergence at jump discontinuities and Gibbs phenomenon.
 $L^2$theory: orthonormal sequences and bases. Best approximations, Bessel’s inequality, Parseval’s identity and convergence in $L^2$.
 Applications to partial differential equations: the heat and wave equations on an interval with constant boundary conditions, the Dirichlet problem for the Laplace equation on the disk, the Poisson kernel.
Bibliography:
 Korner, Fourier analysis
 Stein and Shakarchi, Fourier analysis
The aim of the course is to study main principles of probability theory. Such themes as probability spaces, random variables, probability distributions are given in details.Some applications are also considered.1. Probability space: sample space, probability function, finite symmetric probability space, combinatorial methods, and geometrical probabilities.2. Conditional probability, independent events, total probability formula, Bayes formula. 3. Discrete random variable, special distributions: uniform, binomial, geometric, negative binomial, hypergeometric and Poisson distribution. Poisson process.4. Continuous random variable, density function, cummulative distribution function. Special distributions: uniform, exponential, gamma and normal. Transformations of random variables. Distribution of maximum and minimum. Random variable of mixed type.5. Moments of random variable. Expectation and variance. Chebyshev inequality.6. Random vector, joint probability function, joint density function, marginal distributions. Conditional density, covariance and correlation coefficient.7. Central Limit Theorem. Normal approximation. Law of Large Numbers.
2022–23–B
Lecturer: Dr. Dennis Gulko
סילבוס:

קבוצות: שייכות, איחוד, חיתוך, הפרש.

מכפלה קרטזית, מושג היחס, יחסי שקילות, יחס סדר חלקי, יחס סדר קווי. הגדרת פונקציה כקבוצת סדורים.

תחשיב הפסוקים: ו/או גרירה, שקילות וטבלאות האמת שלהם, ערך האמת של פסוקים בהשמה, שקילות לוגית וגרירה לוגית, טאוטולוגיות ופסוקים שקריים, הטאוטולוגיות החשובות: למשל, חוקי הפילוג, ונוסחאות דהמורגן.

תחשיב הפרדיקטים: הגדרת שפת תחשיב הפרדיקטים ומשמעותה; הגדרת מבנים; נוסחאות ופסוקים; הסתפקות במבנה ובהשמה, אמיתיות לוגית, גרירה לוגית, שקילות לוגית; השקילויות החשובות, סדר הכמתים, הכנסת השלילה פנימה.

תורת הקבוצות: התאמות חדחדערכיות, הרכבת פונקציות והפונקציה ההפוכה; יחסי שקילות; הגדרת העוצמה, שיוויון עוצמות ואישיוויון עוצמות; משפט קנטור ברנשטיין (ללא הוכחה), המשפט שכל שתי עוצמות נתנות להשוואה (ללא הוכחה); משפט קנטור על עוצמת קבוצות החזקה $\mathbb{R}=\mathcal{P}(\mathbb{N})$, $\mathbb{Q}=\mathbb{N}\times\mathbb{N}=\mathbb{N}$.
2022–23–B
Lecturer: Dr. Matan ZivAv
1) Probability space 2) Law of total probability 3) Conditional probability, independent events 4) Bayes’ law 5) Discrete random variables. Discrete distributions: uniform, Bernoulli, binomial, geometric, Poisson 6) Continuous random variable. Continuous distributions: uniform, exponential, normal 7) Discrete twodimensional joint random variables 8) Independence of random variables 9) Expectation 10) Variance, covariance, correlation coefficient
 The Riemann integral: Riemann sums, the fundamental theorem of calculus and the indefinite integral. Methods for computing integrals: integration by parts, substitution, partial fractions. Improper integrals and application to series. 2. Uniform and pointwise convergence. Cauchy criterion and the Weierstrass Mtest. Power series. Taylor series. 3. First order ODE’s: initial value problem, local uniqueness and existence theorem. Explicit solutions: linear, separable and homogeneous equations, Bernoulli equations. 4. Systems of ODE’s. Uniqueness and existence (without proof). Homogeneous systems of linear ODE’s with constant coefficients. 5. Higher order ODE’s: uniqueness and existence theorem (without proof), basic theory. The method of undetermined coefficients for inhomogeneous second order linear equations with constant coefficients. The harmonic oscillator and/or RLC circuits. If time permits: variation of parameters, Wronskian theory.
2022–23–B
Lecturer: Dr. Dennis Gulko
Free oscillations of simple systems (including linearity and superposition principle). Free oscillations of systems with many degrees of freedom (including vibrations of a string and Fourier analysis). Driven oscillations. Traveling waves. Reflection and Transmission. Modulations, pulses, and wave packets. Waves in two and three dimensions. Polarizations. Linear optics. Interference and diffraction. The particle properties of waves. The atom and the wave properties of particles. Elementary particles
 Real numbers. Supremum and Infimum of a set. 2. Convergent sequences, subsequences, Cauchy sequences. The BolzanoWeierstrass theorem. Limit superior and limit inferior. 3. Series. Partial sums, convergent and divergent series, Cauchy criterion. Series of nonnegative terms. The root and the ratio tests. Conditional and absolute convergence. The Leibnitz test for series with alternating signs. Rearrangements of series (without proof) 4. The limit of a function. Continuous functions. Continuity of the elementary functions. Properties of functions continuous on a closed interval: boundedness and attainment of extrema. Uniform continuity, Cantor?s theorem. 5. The derivative of a function. Mean value theorems. Derivatives of higher order. L’Hospital’s rule. Taylor’s theorem. Lagrange remainder formula.
2022–23–B
Lecturer: Prof. Arkady Leiderman
Hours:  יום ב 10:00  08:00 in צוקר, גולדשטייןגורן [72] חדר 212  יום ד 10:00  08:00 in צוקר, גולדשטייןגורן [72] חדר 212
 Review of Logic
 Crieteria for completeness of axiom systems
 Review of Turing machines computability
 The recursion theorem and its uses
 Formal systems and arithmetization of syntax
 representation of recursive function in arithmetic
 Godel’s fixedpoint theorem. First incompleteness theorem
 Loeb’s theorem and Godel’s second incompleteness theorem
 Additional advanced material
Basic concepts, direction fields. First order differential equations. Separable and exact equations, integrating factors. Methods for finding explicit solutions, Bernoulli equations. Euler approximations. Examples, polulation growth. Second order differential equations. Equations with constant coefficients, the solution space, the Wronskian. Nonhomogeneous equations. Variation of parameters. Systems of two first order equations with constant coefficients. Examples and applications.
 Descriptive statistics: organizing, processing and displaying data. 2. Sampling distributions: Normal distribution, the student tdistribution, ChiSquare distribution and Fisher’s Fdistribution 3. Estimation: A point estimate and Confidence Interval of population parameters: Mean variance and proportion. Tolerance interval. 4. Testing hypothesis about a population’s parameters: Mean, variance and proportion. 5. Evaluating the properties of a statistical test: errors, significance level and power of a test. 6. Testing hypothesis about equality of variances, equality of means and equality of proportions of two populations. 7. Testing for independence of factors: Normal and ChiSquare methods. 8. Testing for goodness of fit of data to a probabilistic model: ChiSquare test. 9. Linear regression: Inference about the utility of a linear regression model. Covariance and correlation coefficient. Confidence and prediction intervals. 10. Weibull distribution: estimating the distribution’s parameters
 Introduction: Actions of groups on sets. Induced linear actions. Multilinear algebra.
 Representations of groups, direct sum. Irreducible representations, semisimple representations. Schur’s lemma. Irreducible representations of finite abelian groups. Complete reducibility, Machke’s theorem.
 Equivalent representations. Morphisms between representations. The category of representations of a finite group. A description using the group ring. Multilinear algebra of representations: dual representation, tensor product (inner and outer).
 Decomposition of the regular representation into irreducible representations. The number of irreducibles is equal to the number of conjugacy classes. Matrix coefficients, characters, orthogonality.
 Harmonic analysis: Fourier transform on finite groups and the noncommutative Fourier transform.
 Frobenius divisibility and Burnside $p^aq^b$ theorem.
 Constructions of representations: induced representations. Frobenius reciprocity. The character of induced representation. Mackey’s formula. Mackey’s method for representations of semidirect products.
 Induction functor: as adjoint to restrictions, relation to tensor product. Restriction problems, multiplicity problems, Gelfand pairs and relative representation theory.
 Examples of representations of specific groups: $SL(2)$ over finite fields, Icosahedron group, Symmetric groups.
 Artin and Brauer Theorems on monomial representations
The main purpose of the course is to introduce students to the basic concepts and theorems of Functional Analysis, and to develop an “abstract geometrical” approach to different mathematical problems.
 Metric spaces. Properties of complete metric spaces. Mappings of metric spaces. Fixed points. Application to ODE. Compact sets in metric spaces.
 Normed spaces. Holder’s inequality. The spaces: $C[0,1]$, $l_p$, $c_0$, $c$, $L_p$. Banach spaces. The completion theorem. Completeness of the above spaces.
 Linear operators. The norm of a linear operator. Isomorphism and isometry. Any two $n$dimensional normed spaces are isomorphic. The open mapping theorem and the closed graph theorem. Uniform boundedness principle. The completeness of the space $L(X, Y)$ of all linear bounded operators acting from a normed space $X$ into a Banach space $Y$.
 Linear functionals. The Hahn–Banach extension theorem. The dual space. Dual spaces for the examples above. Adjoint operators.
 Quotient space. Completeness of the quotient space. The dual space for a quotient space. Reflexive spaces.
 Hilbert spaces. The Cauchy–Schwarz inequality. Orthogonal projectors. Orthonormal basis. Any two separable infinitedimensional Hilbert spaces are isometric. Closed operators in a Hilbert space.
 An axiom system for predicate calculus and the completeness theorem.
 Introduction to model theory: The compactness Theorem, Skolem–Löwenheim Theorems, elementary substructures.
 Decidability and undecidability of theories, Gödel first Incompleteness Theorem.
 Introduction: overall description of the multipurpose digital computer operation, the central processing unit (CPU) organization, representation and processing of information (data and programs).
 Machine language: representation of operations and commands, selecting the command set, designing the command architecture, addressing modes.
 Control Unit: application methods by logic and microprogramming, interpretation and execution of command set.
 Arithmetic Unit: numbers representation, basic arithmetic operations in digital systems, addition, subtraction, multiplication and division of fix and floating point numbers.
 Memory Unit: memory types, addressing methods, memory organization, virtual memory, cache memory.
 Input/Output Unit: I/O addressing modes, I/O data transferring control by serial polling, interrupt and direct memory access (DMA).
(1) Probability space.(2) Conditional probability, independent events, Bayes’s theorem, complete probabilities.(3) Random discrete variable, discrete distributions: uniform, binomial, geometric, hypergeometric, negative binomial, Poisson.(4) Random continuos variable, continuos distributions: uniform, exponential, normal.(5) Random discrete two dimensional variable, independence of variables.(6) Mean, variance, correlation coefficient.(7) Chebyshev inequalitiy, large numbers law.(8) Central Limit Theorem, normal approximation.
Hilbert space. The probability matrix. Unitary operations: Displacements, Rotations, Boosts, Gauge. Unitary representations of rotations and the spin concept. Actual procedure for building rotation matrices. Addition of angular momentum (elementary). Perturbation theory. Wigner decay. Fermi golden rule. The adiabatic equation. Particle in a few site system. Particle in 1D ring and AB effect. Particle in a uniform magnetic field (Landau, Hall). Spin a in magnetic field.
 Introduction: Sets, subsets, permutations, functions, partitions. Indistinguishable elements, multisets, binary algebra of subsets. Rules of sum and product, convolutions, counting pairs. Binomial and multinomial coefficients. Stirling numbers of second kind, definition and a recurrenat formula.
 Graphs: General notions and examples. Isomorphism. Connectivity. Euler graphs. Trees. Cayley’s theorem. Bipartite graphs. Konig’s theorem, P. Hall’s theorem.
 The inclusionexclusion method: The complete inclusionexclusion theorem. An explicit formula for the Stirling numbers. Counting permutations under constraints, rook polynomials.
 Generating functions: General notion, combinatorial meaning of operations on generating functions. Theory of recurrence equations with constant coefficients: the general solution of the homogeneous equation, general and special cases of nonhomogeneity. Catalan numbers. Partitions of numbers, Ferrers diagrams. Exponential generating functions, counting words, set partitions, etc.
 Groups as symmetries. Examples: cyclic, dihedral, symmetric, matrix groups.
 Homomorphism. Subgroups and normal subgroups. Quotient groups. Lagrange’s theorem. The isomorphism theorems. Direct products of groups.
 Actions of groups on sets. Cayley’s theorem.
 Group automorphisms.
 Sylow’s theorems. Application: classification of groups of small order.
 Composition series and Jordan–Hoelder theorem. Solvable groups.
 Classification of finite abelian groups, finitelygenerated abelian groups.
 Symmetric group and alternating group. The alternating group is simple.
 Rings, maximal and prime ideals, integral domain, quotient ring. Homomorphism theorems.
 Multilinear algebra: Quotient spaces. Tensor products of vector spaces. Action of $S_n$ on tensor powers. Exterior and symmetric algebras. Multilinear forms and determinant.
 Optional topics: group of symmetries of platonic solids, free groups, semidirect products, representation theory of finite groups.
 Cesaro means: Convolutions, positive summability kernels and Fejer’s theorem.
 Applications of Fejer’s theorem: the Weierstrass approximation theorem for polynomials, Weyl’s equidistribution theorem, construction of a nowhere differentiable function (time permitting).
 Pointwise and uniform convergence and divergence of partial sums: the Dirichlet kernel and its properties, construction of a continuous function with divergent Fourier series, the Dini test.
 $L^2$ approximations. Parseval’s formula. Absolute convergence of Fourier series of $C^1$ functions. Time permitting, the isoperimetric problem or other applications.
 Applications to partial differential equations. The heat and wave equation on the circle and on the interval. The Poisson kernel and the Laplace equation on the disk.
 Fourier series of linear functionals on $C^n(\mathbb{T})$. The notion of a distribution on the circle.
 Time permitting: positive definite sequences and Herglotz’s theorem.
 The Fourier transform: convolutions, the inversion formula, Plancherel’s theorem, Hermite functions. Time permitting: tempered distributions, further applications to differential equations.
 Fourier analysis on finite cyclic groups, and the Fast Fourier Transform algorithm.
 Lines and planes. Cross product. Vector valued functions of a single variable, curves in the plane, tangents, motion on a curve.
 Functions of several variables: open and closed sets, limits, continuity, differentiability, directional derivatives, partial derivatives, the gradient, scalar and vector fields, the chain rule, the Jacobian. Implicit differentiation and the implicit function theorem. Extremum problems in the plane and in space: the Hessian and the second derivatives test, Lagrange multipliers.
 Line integrals in the plane and in space, definition and basic properties, work, independence from the path, connection to the gradient, conservative vector field, construction of potential functions. Applications to ODEs: exact equations and integrating factors. Line integral of second kind and arclength.
 Double and triple integrals: definition and basic properties, Fubini theorem. Change of variable and the Jacobian, polar coordinates in the plane and cylindrical and spherical coordinates in space. Green’s theorem in the plane.
 Parametric representation of surfaces in space, normals, the area of a parametrized surface, surface integrals including reparametrizations
 Curl and divergence of vector fields. The theorems of Gauss and Stokes.
2022–23–B
Lecturer: Dr. Ishai DanCohen
Hours:  יום ב 16:00  14:00 in גולדברגר [28] חדר 205  יום ה 11:00  09:00 in צוקר, גולדשטייןגורן [72] חדר 487

מרחבים נורמיים ומרחבי מכפלה פנימית, הקירוב הטוב ביותר והטלות אורתוגונליות, מערכות אורתונורמליות. התכנסות במרחבים נורמיים. מערכות אורתונורמליות אינסופיות, שוויון פרסבל ומערכות אורתונורמליות שלמות.

פולינומים אורתוגונליים. משפט הקירוב של ויירשטראס. שלמות של פולינומים אורתוגונליים בקטע סופי.

טורי פורייה. שלמות, התכנסות נקודתית ותנאים להתכנסות במידה שווה.

טרנספורם פורייה. משפט פלנשרל. נוסחת ההיפוך של פורייה. קונבולוציות. פולינומי הרמיט.

משוואות שטורםליוביל בקטע סופי. אורתוגונליות של פונקציות עצמיות. קיום ושלמות של מערכת פונקציות עצמיות עבור בעיית שטורםליוביל רגולארית (עם הוכחה חלקית).
ביבליוגרפיה:

Hartman, Philip. Ordinary differential equations. Corrected reprint of the second (1982) edition. With a foreword by Peter Bates. Classics in Applied Mathematics, 38. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2002.

Jackson, Dunham. Fourier series and orthogonal polynomials. Reprint of the 1941 original. Dover Publications, Inc., Mineola, NY, 2004.

K?rner, T. W. Fourier analysis. Second edition. Cambridge University Press, Cambridge, 1989.
FirstOrder PDE Cauchy Problem Method of Characteristics The Wave Equation: Vibrations of an Elastic String D’Alembert’s Solution Fourier Series Fourier Sine Series InitialBoundary Value Problems The Wave Equation .Separation of Variables Fourier Series Solution of the Heat Equation The Heat Equation. Duhamel’s Principle. Laplace’s Equation. Dirichlet Problem for a Disc
2022–23–B
Lecturer: Ms. Tamar Pundik
The derivative as a function: continuously differentiable functions, Darboux’s theorem. Convex functions: definition, onesided differentiability, connection to the second derivative. Cauchy’s generalized Mean Value Theorem and its applications: L’Hospital’s rule, Taylor polynomials with Lagrange remainder. The NewtonRaphson method. Series: Cauchy’s criterion, absolutely convergent series, the comparison, quotient and root tests, the Dirichlet test, change of the order of summation, the product formula for series, Taylor series, Taylor series of elementary functions. The definition of an analytic function, the radius of convergence of a power series. The Riemann integral. Riemann sums. The fundamental theorem of calculus (the NewtonLeibniz formula). Methods for computing integrals (the indefinite integral): integration by parts, change of variable, partial fractions. Improper integrals. Numerical integration: the midpoint, trapezoid and Simpson’s rules. Stirling’s formula. Introduction to convergence of functions, problems with pointwise convergence. Introduction to ordinary differential equations: the differential equation y’=ky, solution of first order ODE’s by separation of variables, initial value conditions.
2022–23–B
Lecturer: Prof. Eitan Sayag
Hours:  יום ב 14:00  12:00 in ביה"ס אילנות [97] חדר 202  יום ד 14:00  12:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 16
2022–23–B
Lecturer: Dr. Luba Sapir
Hours:  יום ג 12:00  10:00 in בניין כתות לימוד [35] חדר 1  יום ה 12:00  10:00 in בניין כתות לימוד [35] חדר 1
 The notion of a Dynamical system, background, motivating examples.
 Limit Sets and Recurrence, Van der Varden’s theorem as an application.
 Basic notions in topological dynamics: Topological transitivity, minimality, topological Mixing, Expansiveness, Equicontinuity.
 Circle rotations, homeomorphisms of the circle, rotation numbers and Denjoy’s theorem.
 Topological entropy.
 Automorphisms of the torus, automorphisms of compact groups.
 Shift spaces and shifts of finite type
 Brief introduction to ergodic theory: Equidistribution, unique ergodicity.
 Complex numbers. Fields: definition and properties. Examples.
 Systems of Linear equations. Gauss elimination process.
 Matrices and operations on them. Invertible matrices.
 Determinant: definition and properties. Adjoint matrix. Cramer rule.
 Vector spaces and subspaces. Linear spanning and linear dependence. Basis and dimension. Coordinates with respect to a given basis.
 Linear transformations. Kernel and Image. Isomorphism of vector spaces. Matrix of a linear transformation with respect to given bases.
 The space of linear transformations between two vector spaces. Dual space
 Fields: basic properties and examples, the characteristic, prime fields
 Polynomials: irreducibility, the Eisenstein criterion, Gauss’s lemma
 Extensions of fields: the tower property, algebraic and transcendental extensions, adjoining an element to a field
 Ruler and compass constructions
 Algebraic closures: existence and uniqueness
 Splitting fields
 Galois extensions: automorphisms, normality, separability, fixed fields, Galois groups, the fundamental theorem of Galois theory.
 Cyclic extensions
 Solving polynomial equations by radicals: the Galois group of a polynomial, the discriminant, the CardanoTartaglia method, solvable groups, Galois theorem
 Roots of unity: cyclotomic fields, the cyclotomic polynomials and their irreducibility
 Finite fields: existence and uniqueness, Galois groups over finite fields, primitive elements
2022–23–B
Lecturer: Prof. Ido Efrat
Hours:  יום ב 14:00  12:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 207  יום ד 11:00  09:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 303
 Complex numbers. Analytic functions, Cauchy–Riemann equations.
 Conformal mappings, Mobius transformations.
 Integration. Cauchy Theorem. Cauchy integral formula. Zeroes, poles, Taylor series, Laurent series. Residue calculus.
 The theorems of Weierstrass and of MittagLeffler. Entire functions. Normal families.
 Riemann Mapping Theorem. Harmonic functions, Dirichlet problem.
2022–23–B
Lecturer: Dr. David Corwin
Hours:  יום א 16:00  14:00 in גולדברגר [28] חדר 107  יום ג 12:00  10:00 in גולדברגר [28] חדר 103
Axioms of the reals. Sequences: limits, monotone sequences, the BolzanoWeierstrass theorem, Cauchy’s criterion, the number e. Limits of functions. Continuous functions: equivalent definitions of continuity, properties of the elementary functions, the exponential function, the Intermediate Value Theorem, existence of extrema in closed and bounded sets, uniform continuity and Cantor’s theorem. Introduction to derivatives: the definition of the derivative and differentiation rules, the derivative of an inverse function, derivatives of elementary functions, Fermat’s theorem, Rolle’s theorem and Lagrange’s Mean Value Theorem.
 Ordinary differential equations: explicit solutions of first order equations. 2nd order equations. Higher order ordinary differential equations. Systems of ordinary differential equations.
 Fourier series: Review of series of functions, Fourier expansions and properties of Fourier series, convergence of Fourier series, Gibbs phenomenon. Application to periodic ODE’s.
 The Laplace transform and applications to ODE’s.
Sample spaces and finite probability spaces with symmetric simple events, general probabilty spaces and the fields of events, the Borel filed and probabilities on it defined by densities, conditional probabilities and independent events, random variables and their distribution functions (discrete, absolutely continuous, mixed), the expectation of a random variable (for discrete, absolutely continuous and general distribution), the variance of a random variable, random vectors and the covariance, independent random variables, the central limit theorem for i.i.d. random variables, examples related to analysis of simple algorithms, joint densities (discrete or continuous) with computation of the covariance and the marginal distributions, the weak law of large numbers.1. A.M. Mood, F.A. Graybill And D.C.Boes. Introduction To The Theory Of Statistics 3rd Edition, McgrawHill, 1974. 2. A. Dvoretzky, Probability theory (in Hebrew), Academon, Jerusalem, 1968.3. B. Gnedenko, The theory of Probability, Chelsea 1967 (or Moscow 1982) in English; Russian origina titled ‘A course in probability theory”.
(1) Parametric point estimation: sampling, statistics, estimators, sample mean, sample variance, method of moments, maximum likelihood.(2) Properties of point estimators: mean square error, loss and risk functions, unbiased estimators, lower bound for variance (RaoCramer inequality), efficient estimators.(3) Sufficient statistics: factorization criterion, sufficiency and completeness, exponential families of distributions, finding of minimal variance unbiased estimators.(4) Parametric interval estimation: confidence interval, sampling from normal distribution, confidence intervals for mean and variance, large sample confidence interval.(5) Test of hypotheses: simple hypothese versus simple alternative, most powerful test, composite hypotheses, uniformly most powerful test, sampling from normal distribution, tests for mean and variance.(6) Comparison of two populations: confidence interval for differences of two means, test on two means, tests concerning variances, goodness of fit tests, tests on independence.
This course aims to provide algorithmic and numerical tools for solving common computational optimization problems in science and engineering. The course will include: linear algebra reminder: norms, least squares, eigenvalue and singular value decompositions, optimization of quadratic problems. Convexity, iterative methods for unconstrained optimization (Steepest Descent, Newton, QuasiNewton, Conjugate Gradients, subspace methods, BFGS), and linesearch methods. Constrained optimization with equality and inequality constraints (method of Lagrange multipliers and KKT optimality conditions), linear and quadratic programming, penalty, barrier and projection methods. Duality. Splitting methods and ADMM. Introduction to stochastic optimization (SGD). Introduction to nonsmooth and sparse optimization. Robust statistics for dealing with outliers. The assignments in this course will include writing computer programs for practically implementing and demonstrating the algorithms taught in this course.
Goal: To teach the basic methods of numerical algebraic geometry that allow to find all solutions to a system of polynomial equations over the complex numbers and applications of these methods Topics: Polynomials in several variables, The zero set, Resultants, Elimination and Bezut theorems, The Newton method in several variables, Homotopy continuation and finding isolated solutions, Computation of higher dimensional components, Applications in robotics and elsewhere.
An introduction to the basic notions of probability theory:
sample spaces limits of events conditional probability independent events sigma algebras, continuous spaces, Lebesgue measure random variables and distributions independence expectation variance and covariance convergence of random variables: almostsure, in Lp, in probability law of large numbers convergence in law central limit theorem
 Review of probability: a. Basic notions. b. Random variables, Transformation of random variables, Independence. c. Expectation, Variance, Covariance. Conditional Expectation.
 Probability inequalities: Mean estimation, Hoeffding?s inequality.
 Convergence of random variables: a. Types of convergence. b. The law of large numbers. c. The central limit theorem.
 Statistical inference: a. Introduction. b. Parametric and nonparametric models. c. Point estimation, confidence interval and hypothesis testing.
 Parametric point estimation: a. Methods for finding estimators: method of moments; maximum likelihood; other methods. b. Properties of point estimators: bias; mean square error; consistency c. Properties of maximum likelihood estimators. d. Computing of maximum likelihood estimate
 Parametric interval estimation a. Introduction. b. Pivotal Quantity. c. Sampling from the normal distribution: confidence interval for mean, variance. d. Largesample confidence intervals.
 Hypothesis testing concepts: parametric vs. nonparametric a. Introduction and main definitions. b. Sampling from the Normal distribution. c. pvalues. d. Chisquare distribution and tests. e. Goodnessoffit tests. f. Tests of independence. g. Empirical cumulative distribution function. KolmogorovSmirnov Goodnessof fit test.
 Regression. a. Simple linear regression. b. Least Squares and Maximum Likelihood. c. Properties of least Squares estimators. d. Prediction.
 Handling noisy data, outliers.
 Introduction to number theory. Intervals and segments. Concept of a function. Elementary functions. 2. Limit of a function.3. Continuity and discontinuity of functions.4. Derivative and differential. Basic derivatives. Differentiability and continuity. Linear approximation by differentials. Highorder derivatives. The fundamental theorems of differentiation and their applications. L’Hopital’s theorem and its application to calculation of limits.5. Taylor’s polynom. Expansion of functions into Taylor’s and McLoran’s series. Expansions of some usage functions. Application of Taylor’s and McLoran’s polynoms a) to approximate calculations, and b) to calculation of limits.6. Investigation of a function. Extremal points. Necessary and sufficient conditions for extrema. Max. and min. of a function within a segment. Convexity and concavity, inflection point. Asymptotes. Graph construction.7. Primitive function and indefinite integral. Table integrals. Calculation of indefinite integrals by decomposition, by parts, by substitution. Integration of rational and trigonometric functions.8. Definite integrals. Reimann’s sum. The fundamental theorem. Formula of NewtonLeibnitz. Calculation of definite integrals. Integration by decomposition, by parts, by substitution.9. Use in definite integrals to calculation of areas, volumes and curve lengthes. Rectungular and polar coordinate systems.10. Firstorder ordinary differential equations. General definitions. Cauchy problem. Separated variables.
The topology and geometry of the group $SL(2,\mathbb{R})$, discrete subgroups, relation to Riemann Surfaces
Topics
 general introduction to representation theory
 classification of the unitary representation of $SL(2,\mathbb{R})$
 applications of representation theory to Harmonic analysis on various classical spaces.
 HoweMoore theorem and its applications for uniform distributions and mixing.
 The space of Lattices and functions on Lattices.
 Modular forms and automorphic forms.
 The theory of automorphic forms. Selberg’s trace formula and applications.
Bibliography
 J.P. Serre, A course in Arithmetic. GTM, vol. 7
 Lang, $SL(2,\mathbb{R})$, GTM
 Sarnak, Some applications of modular forms, Cambridge Tracts in Mathematics
Vector analysis. electric field (including Coulomb law, Gauss law, and first Maxwell equation). Electric potential (including Laplace and Poisson equations, electric dipole). Conductors (including capacity and capacitors). Electric current (including Ohm’s law, direct current circuits, RC circuit). Magnetic field I (including special relativity connections, Lorenz force, Ampere force, magnetic moment). Magnetic field II (including vector potential, BioSavard’s law, Ampere’s law, absence of magnetic charge, second Maxwell equation, truncated third Maxwell equation, gauge, field of magnetic moment, solenoids). Electromagnetic induction (including fourth Maxwell equation, Lenz law, inductance, AC current circuits). Maxwell equations and electromagnetic waves. (Optional) Electric and magnetic properties of materials
 Ordered fields: Hilbert’s 17 problem, orderings and preorderings, sums of squares, real closed fields, ArtinSchreier theory
 Infinite Galois theory: profinite groups, the Galois correspondence in the infinite case
 Introduction to Galois cohomology
Review of Newton laws. Generalized coordinates. The principle of least action, constraints. The Lagrangian for free particle and for a system of particles. Conservation laws (energy, momentum, angular momentum). The virial theorem. Motion in one dimension. The two body problem. Motion in a central field and Kepler’s problem. Scattering, total and differential cross sections. Rutherford’s formula. Small oscillations, forced and damped oscillations. Motion of a rigid body, the Inertia tensor. Space and body frames the Coriolis effect. 1The rotation group, infinitesimal and finite rotations. Euler’s angles, and Euler’s equations. Symmetric and asymmetric tops. The Hamiltonian and Hamilton’s equations. Canonical transformations and the Poisson brackets. Liouville’s theorem. The HamiltonJacobi equation.
 Infinite series. Tests for convergence. Taylor series and Taylor polynomials. Absolute convergence. Alternating series. Conditional convergence. Power series for functions. Convergence of power series; differentiation and integration.
 Vectors and parametric equations. Parametric equation in analytic geometry. Space coordinates. Vectors in space. The scalar product of two vectors. The vector product of two vectors in space. Equations of lines and planes. product of three vectors and more. Catalog of the quadratic surfaces. Cylindres.
 Vector functions and their derivatives. Vector functions. differentiation formulas. Velocity and acceleration. Tangential vectors. Curvature and normal vectors. Polar coordinates.
 Partial differentiation. Functions of two and more variables. The directional derivative. limits and continuity. Tangent plane and normal lines. The gradient. The chain rule for partial derivatives. The total differentiation. Maxima and minima of functions of several independent variables. Higher order derivatives.
 Multiple integrals. Double integrals. Area and volume by double integrals. Double integrals in polar coordinates. Physical applications. triple integrals. Integration in cylindrical and spherical coordinates. Surface area. Change of variable in multiple integrals.
 Vector analysis. Vector fields. Line integrals. Independence of path. Green’s theorem. Surface integrals. The divergence theorem. Stokes’ theorem.
The aim of the course is to expose students to key events in the history of mathematics throughout history from the point of view of modern mathematics and, if possible, to link these events to the content studied within the framework of the degree in mathematics. The study will include getting to know the names and histories of major mathematicians throughout history and discussing their contributions to the development of the various branches of mathematics as we know them today. Alongside this there will be a discussion of the development of ideas and concepts in mathematics over the generations to the present day.
Ordinary differential equations: explicit solutions of first order equations. 2nd order equations. Higher order ordinary differential equations. Systems of ordinary differential equations. 2. Fourier series: Review of series of functions, Fourier expansions and properties of Fourier series, convergence of Fourier series, Gibbs phenomenon. Application to the heat conduction equation. 3. Additional applications as time permits.
Mathematical reminder: Vector calculus. Maxwell equations in vacuum, continuity and boundary conditions. Electrostatics in free space, Green’s function. Electrostatics with boundaries, Laplace equation for various symmetries. Magnetostatics. Electrodynamics, induction, conservation laws. Electromagnetic waves. Relativistic electrodynamics. Fields of moving charges. Radiation
Photoelectric effect, FrankHertz experiment, Compton effect, Twoslit experiment, Particlewave duality, Bohr model, Periodic table Schrodinger eq, Operators, Harmonic oscillator, Stepwise potentials, Quantum mechanics in Hilbert space, Commutator, Central forces, Angular momentum, Hydrogen atom, Magnetic field and spin
Main concepts of Statistical Physics: separation of microscopic and macroscopic description of a system; microstates vs. configurations; sharpness of the distribution of microstates over configuration space; basic idea of statistical ensembles and of probabilities. Main concepts of Statistical Physics as applied to a simple modelsystem of twostate spins: numbering of microscopic states; multiplicity function; continuous approximation of the multiplicity function; probabilities of micro and macro states; characteristics of probability function, mean, variance and standard deviation; sharpness of the probability distribution of a macroscopic variable; correlation of two random variables. Microcanonical ensemble: probabilities of microscopic states in an isolated system at equilibrium; example of equilibration in the system of spins; thermal equilibrium of two arbitrary systems; definition of temperature; entropy in microcanonical ensemble; additivity of entropy; the postulate of entropy increase in an isolated system (second Law of Thermodynamics); the direction of heat flow. Canonical ensemble: Boltzmann Distribution; partition function; microscopic state probabilities; average energy of a system in thermal equilibrium; fluctuations in energy and their relation to heat capacity; Helmoholtz Free energy and its relation to the partition function. Applications of the canonical ensemble: the system of spins; Schottky anomaly; partition function, energy and free energy of a classical ideal monoatomic gas. Thermodynamic equilibrium and thermodynamic processes; characteristic time scales; reversible and irreversible processes; quasistationary process; examples of quasistationary heat transfer and of work. Heat and Work in thermodynamics; First Law of Thermodynamics; differential relations between thermodynamic quantities; Maxwell relations; enthalpy; heat capacity; intensive and extensive quantities; entropy, pressure and heat capacity of a classical ideal
Introduction to computer science 20111011 1. Basic Programming 2. Some order and arrays 3. Arrays and functions 4. Strings, Binary search and Selection sort 5. Insertion sort, handling matrices 6. Introduction to recursive programming 7. Recursive programming : binary search, selection sort, Merge sort 8. Last examples : recursive programming 9. Introduction to object oriented programming 10. Object oriented programming 11. Lecture canceled 12. Interfaces 13. Inheritance 14. Inheritance and abstract inheritance 15. Exceptions 16. Data structures 17. Stack application 18. Queue and linked lists 19. Lists  the recursice perspective 20. Trees
Number representation in different bases, binary codes and binary arithmetic. Combinational systems: Boolean algebra, switching function representations and minimization. Karnaugh maps, prime implicate table. Hazards. Combinational circuit design. Switching devices: logic gates (NAND, AND, NOR, OR, NOT, XOR); modules: HA, FA, HS, FS, Multipliers, Decoders, Multiplexers, Demultiplexers, PROM, PLA. Using modules to the implementation of combinational circuits. Sequential systems: Basic models, synchronous and asynchronous system structure. Bistable memory devices (Latches, FlipFlops), transition table and state table. MasterSlave FlipFlops, EdgeTriggered FlipFlops. Design and implementation of sequential systems, synchronous and asynchronous. Race problem solution. Special modules: Registers, Shift Registers, Counters.
Thermodynamic potentials: Gibbs free energy; per molecule and per unit volume thermodynamic potentials. First order phase transitions: experimental observations; thermodynamic phases; free energy per unit volume; conditions for phase separation; local and global stability of a thermodynamic phase; graphical representation. Molecular interactions: hardcore repulsion; Van der Waals attraction; induced dipole; electronic and dipole polarizability; estimations of Van der Waals attraction for simple molecules; LennardJones potential. Incorporation of molecular interactions into free energy: phase coexistence; common tangent construction; phase diagrams; spinodal and binodal lines; critical temperature; ClausiusClapeyron equation. Van der Waals gas: free energy; pressure; spinodal; critical point; estimations of critical temperature; approximation of binodal far from the critical point. Van der Waals gas near the critical point: free energy; binodal line; first and secondorder phase transition; critical opalescence; nucleation and growth; surface energy; critical nucleous. Ferromagnetparamagnet secondorder phase transition; Landau theory of phase transitions. Kinetic theory of gases: characteristic velocities; kinetic derivation of pressure of ideal gas; Maxwell distribution of velocities. Diffusion and random walks on a lattice model: meansquare displacement; diffusion coefficient; the diffusion equation for random walks; diffusion as transport phenomenon; derivation of Fick’s laws from microscopic considerations; self diffusion and collective diffusion coefficients. Diffusion in gas: random walks; distribution of free paths; mean free path; mean square displacement of a molecule; selfdiffusion coefficient; diffusion in concentration gradient; derivation of Fick’s laws; equivalence of selfdiffusion and collective diffusion coefficients for gases. Other linear transport phenomena in gases: heat conductivity; Fourier law; thermalization;
 Introduction: the real and complex numbers, polynomials.
 Systems of linear equations and Gauss elimination.
 Vector spaces: examples (Euclidean 2space and 3space, function spaces, matrix spaces), basic concepts, basis and dimension of a vector space. Application to systems of linear equations.
 Inverse matrices, the determinant, scalar products.
 Linear transformations: kernel and image, the matrix representation of a transformation, change of basis.
 Eigenvalues, eigenvectors and diagonalization.
Mathematical introduction: Coordinates, vector algebra, partial derivatives. Particle kinematics: general concepts, velocity, acceleration, rotational motion. Newton’s laws, Galilean relativity, inertial and noninertial frames Particle dynamics: applications of Newton’s laws, work, kinetic energy, momentum, angular momentum. Potential energy, conservation laws. Motion in a potential I: 1D potential, central forces, Keplerian motion. Motion in a potential II: Oscillations. Mathematical addition: complex numbers. Many particle systems, conservation laws, collisions. Rigid body rotation I: Theory (angular velocity and acceleration, tensor of inertia, moment of inertia, torque, angular momentum, kinetic energy). Rigid body rotation II: Applications (rolling, precession, gyroscopes). Basics of special relativity (Optional) Gravitation.
Experiments list: Scanning Tunneling Microscope (STM), Ultra High Vacuum (UHV), Low Energy Electron Diffraction (LEED), X Rays, Magnetic Susceptibility, Mossbauer Effect, Scintillation, Superconductivity, Nuclear Magnetic Resonance (NMR), Hall Effect, Magneto Optical Trapping, Laser Frequency Locking and Atomic Clock. Academic requirements: All students must perform 2 experiments from the list of experiments and submit a report on both of them. There will be preexam prior to each experiment.
סילבוס אינו מוגדר בתקופה המתבקשת
Experiments list: The ratio between electron charge and its mass (e/m), Polarization of light, FrankHertz experiment, Michelson and FabryPerot Interferometers, PhotoElectric Effect, Lasers, Micro Waves, Nuclear Radiation White Noise. Academic requirements: In each semseter all the students must chose and perform 4 experiments from the list of the experiments and submit a report on each of them. There will be preexam prior to each experiment, as well as the final exam at the end of the semester.
 Complex numbers, open sets in the plane.
 Continuity of functions of a complex variable
 Derivative at a point and Cauchy–Riemann equations
 Analytic functions; example of power series and elementary functions
 Cauchy’s theorem and applications.
 Cauchy’s formula and power series expansions
 Morera’s theorem
 Existence of a logarithm and of a square root
 Liouville’s theorem and the fundamental theorem of algebra
 Laurent series and classification of isolated singular points. The residue theorem
 Harmonic functions
 Schwarz’ lemma and applications
 Some ideas on conformal mappings
 Computations of integrals
2022–23–B
Lecturer: Prof. Dmitry Kerner
 Rings. Ring of polynomials and its ideal structure. The prime factorization of a polynomial. Lagrange interpolation.
 Eigenvalues and eigenvectors of linear operators.
 Characteristic polynomial and CayleyHamilton theorem. The primary decomposition theorem. Diagonalization. Nilpotent operators. Jordan decomposition in small dimension Jordan decomposition in general dimension time permitted
 Linear forms. Dual basis. Bilinear forms.
 Inner product spaces. Orthogonal bases. Projections. Adjoint linear transformation. Unitary and Hermitian operators.
 Normal operators and the spectral decomposition theorem. Singular value decomposition theorem and applications.
2022–23–B
Lecturer: Dr. Inna EntovaAizenbud
Hours:  יום א 12:00  10:00 in גולדברגר [28] חדר 106  יום ג 14:00  12:00 in ביה"ס אילנות [97] חדר 202
In this course the basic concepts of onedimensional analysis (a limit, a derivative, an integral) are introduced and explored in different applications: graphing functions, approximations, calculating areas etc.
 Limit of a function, continuity.
 Derivative, basic derivative formulas.
 Derivative of an inverse function; derivative of a composite function, the chain rule; derivative of an implicit function.
 Derivatives of high order.
 The mean value problem theorem. Indeterminate forms and l’Hopital’s rule.
 Rise and fall of a function; local minimal and maximal values of a function.
 Concavity and points of inflection. Asymptotes. Graphing functions.
 Linear approximations and differentials. Teylor’s theorem and approximations of an arbitrary order.
 Indefinite integrals: definition and properties.
 Integration methods: the substitution method, integration by parts.
 Definite integrals. The fundamental theorem of integral calculus (NewtonLeibniz’s theorem).
 Calculating areas.
Bibliography
Thomas & Finney, Calculus and Analytic Geometry, 8th Edition, AddisonWesley (World Student Series).
2022–23–B
Lecturer: Dr. Natalia Gulko
Hours:  יום א 14:00  12:00 in גוטמן [32] חדר 307  יום ד 12:00  10:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 110
 General background: sets and operations on them, Complex numbers: definition (via ordered pairs), addition and multiplication, inverses, adjoint, absolute value. Real and complex polynomials and their roots.
 Fields: Definition, properties, examples: Rationals, reals, complex numbers, integers mod p.
 Linear equations over fields, matrices and elementary row operations, rank of a matrix, solutions of homogeneous and non homogeneous systems of linear equations and the connections between them.
 Vector spaces over fields, subspaces, bases and dimensions, coordinates change of coordinate matrix, row rank as rank of a subspace, sums, direct sums of subspaces and the dimension theorem.
 Matrices multiplication, the algebra of square matrices, inverse determinants: properties, Cramer’s rule, adjoint and its use for finding the inverse.
 Linerar transformationsbasic propertieskernel and image of a linear trasformationrepresentaion of linear transformations by matrices and the effect of change of bases.linear functionals, dual bases
2022–23–B
Lecturer: Dr. Guy Landsman
 Infinite series of nonnegative terms and general series. Absolute and conditional convergence. Power series.
 Vector algebra. Dot product, cross product and box product.
 Analytic geometry of a line and a plane. Parametric equations for a line. Canonic equations for a plane. Points, lines and planes in space.
 Vectorvalued functions. Derivative. Parametrized curves. Tangent lines. Velocity and acceleration. Integration of the equation of motion.
 Surfaces in space. Quadric rotation surfaces. Cylindrical and spherical coordinates.
 Scalar functions of several variables. Scalar field. Level surfaces. Limit and continuity. Partial derivatives. Directional derivative. Gradient vector. Differential. Tangent plane and normal line. Chain rules. Implicit function and its derivative. Taylor and MacLaurin formulas. Local extreme values. Absolute maxima and minima on closed bounded regions.
 Vectorvalued functions of several variables. Vector field. Field curves. Divergence and curl.
 Line and path integrals. Work, circulation. Conservative fields. Potential function.
 Double integral and its applications. Green’s theorem.
 Parametrized surfaces. Tangent plane and normal line. Surface integrals. Flux. Stokes’s theorem.
 Triple integral and its applications. Divergence theorem.
2022–23–B
Lecturer: Prof. Michael Levin
Metric and normed spaces. Equivalence of norms in finite dimensional spaces, the HeineBorel theorem. Convergence of sequences and series of functions: pointwise, uniform, in other norms. Termbyterm differentiation and integration of series of functions, application to power series. Completeness: completeness of the space of continuous functions on a closed interval and a compact metric space. The Weierstrass $M$test. The Baire category theorem and applications, bounded linear functionals and the BanachSteinhaus theorem. Compactness in function spaces and the ArzelaAscoli theorem. Introduction to Fourier series: Cesaro means, convolutions and Fejer’s theorem. The Weierstrass approximation theorem. $L^2$ convergence. Pointwise convergence, the Dirichlet kernel and Dini’s criterion.
 Rings. Ring of polynomials and its ideal structure. The prime factorization of a polynomial. Lagrange interpolation.
 Eigenvalues and eigenvectors of linear operators. Characteristic polynomial and Cayley–Hamilton theorem. The primary decomposition theorem. Diagonalization. Nilpotent operators. Jordan decomposition in small dimension. Jordan decomposition in general dimension time permitting.
 Linear forms. Dual basis. Bilinear forms. Inner product spaces. Orthogonal bases. Projections. Adjoint linear transformation. Unitary and Hermitian operators. Normal operators and the spectral decomposition theorem. Singular value decomposition theorem and applications.
Optional topics:
 Quadratic forms.
 Sylvester theorem.
 Classification of quadrics in twodimensional spaces.
2022–23–B
Lecturer: Prof. Yair Glasner
Basic combinatorics
 Sample space and events. Probability mass function
 Conditional probability. Law of total probability. Bayes? formula
 Independence
 Useful discrete distributions: uniform, binomial, geometric, Poisson, hypergeometric, negative binomial.
 Expectation, variance, median, percentile
 Functions of a random variable
 Two dimensional distributions: joint distribution, marginal distribution, covariance, conditioning on marginals
 Markov, Chebychev and Jensen inequalities
 Normal distribution
 Sequences of random variables: law of large numbers and central limit theorem
 Counting states. Combinatorics. Distinguishable and indistinguishable particles. Ordered and unordered arrangements. Permutations without replacement and with replacement. Combinations without and with replacement. Multisets. N 1/2 spins. Cell gas. Fermions, parafermions, bosons.
 Repeated experiments, outcomes. Frequentist, Bayesian, and Kolmogorov probability, interrelation. Reproducible and irreproducible experiments. Probability in a finite universe. Expanding universe. Time dependent probability. Laws of probability. Mutually exclusive events/outcomes. Conditional probability. Bayes rule. Geometric probability, Betrand paradox.
 Random variables. Discrete random variables. Probability of a random variable. Functions of random variables. Mean, variance, moments in general. Spin 1/2. Paramagnetism. Binomial distribution. Large numbers. Most probable state. Rare events. Radioactive decay. Poisson distribution. Information entropy. Maximum entropy principle without constraints. Uniform distribution. Maximum entropy principle with energy constraint. Boltzmann distribution.
 Gas of particles in the velocity space. Continuous random variable. Probability density. Mean, variance, moments. Deltafunction. Maxwellian (normal, gaussian) distribution. Localized magnetic moment in a magnetic field. Classical paramagnetism. Fluctuations of magnetization. Other observed distributions: merging black holes. Line width: Breit? Wigner distribution. Entropy. Uniform distribution. Particle size distribution of aerosols and mass distribution in the Universe: lognormal distribution. Collisions in accelerators: from binomial distribution to Poisson.
 Multivariate continuous distributions. Joint and marginal distribution. Gas in 3D. Most probable components and most probable magnitude of the velocity. Isotropic and anisotropic distributions. Plasma pressure tensor. Covariance, correlation. Transformation of variables in joint distributions. Beams in plasmas. Cosmic rays: energy spectrum and pitchangle distribution. Covariance vs independence.
 Laws of large numbers. Gaussian as the limiting distribution for the Binomial and and Poisson distributions. Chebyshev’s inequality. Independent random variables. Sum of random variables. Convolution. Convolution of Gaussians. Central limit theorem. Applications and limitations of the theorem: velocity component of air molecules, Coulomb scattering, energy loss of charged particle traversing thin gas layer (Landau distribution).
 Statistics in physics: from data to hypothesis. Main sequence: assume theory, devise an experiment, measure relevant parameters, estimate uncertainties, quantify agreement with theory, accept of reject. Examples: search for Higgs in LHC, dark matter in the Universe. More examples: CP violation.
 Measurements and errors. Propagation of errors. Measured distribution vs true distribution: convolution with the resolution function (detector). Assumption of normal distribution of measurement uncertainties. Distortion of measured distribution: line width.
 Measurements, samples, population, sample statistics. Sample mean and variance. Central limit theorem in statistics. Parameter estimates: frequentist and Bayesian approach. Prior and posterior probabilities. Basic estimators. Maximumlikelihood method: distance from the Sun to the center of the galaxy, mass distribution from LIGO.
 LHC experiments: application of Chisquared. Degrees of freedom. Nuisance parameters. Role of uncertainties (overestimate vs underestimate). Unbiased estimators. Correlation functions.
 Hypothesis testing. Simple and composite hypotheses; statistical tests; Neyman?Pearson; generalised likelihoodratio; Student?s t; Fisher?s F; goodness of fit.
 (Optional) Random walk. Diffusion processes.
 (Optional) MonteCarlo methods.
Metric spaces:
closed sets, open sets, Cauchy sequences, completeness, compactness, Theorem of Heine–Borel, continuity and uniform continuity of functions, uniform convergence of sequences of functions.
Measure theory:
algebras, measures and outer measures, measurable sets, discrete measure spaces, Lebesgue measure on the real line, measurable functions, Lebesgue integral, dominated convergence theorem, $L_p$spaces as complete normed spaces. Timepermitting: signed measures and absolute continuity of measures and the RadonNykodim theorem.
2022–23–B
Lecturer: Prof. Arkady Leiderman
Hours:  יום ג 14:00  12:00 in בנין 90 (מקיף ז') [90] חדר 125  יום ה 14:00  12:00 in גולדברגר [28] חדר 107
 Series of numbers, both positive and general. Absolute and conditional convergence. Root and Ratio tests. Leibniz Alternating series test. 2. Power Series. 3. First order equations: separable equations, exact equations, linear equations, Bernoulli equations. Existence and uniqueness. 4. Second order equations. Reduction of order. Linear homogeneous equations, fundamental solutions and Wronskian. Inhomogeneous equations, variation of parameters. Equations with constant coefficients and the method of undetermined coefficients. Linear equations of higher order. Euler equations. 5. Systems of differential equations.
Basic concepts: Group actions, Cayley graphs and Schreier graphs, The word metric. Quasi isometries. The MilnorSvarc lemma. Free groups and trees: Group presentations. The groupAut(T),elliptic and hyperbolic elements. The boundary of the tree. Covering theory of graphs and the Nielsen Shcreier theorem. The pingpong lemma. Free and amalgamated products, HNN extensions. The group PSL_2 (Z)=Z/2Z*Z/3Z and its action on the Farey tree. Some Hyperbolic geometry: Poincare models, their boundaries. Isometry groupsPSL_2 (R),PSL_2 (C), elliptic, hyperbolic, parabolic and loxodromic isometries and the trace. Free subgroups using the pingpong lemma. Poincare lemma and surface groups as crystallographic hyperbolic groups. The fundamental domain of SL_2 (Z)and the space of lattices. The Farey tessellation and continued fractions. Hyperbolic groups. Gromov hyperbolic spaces and their boundaries. Hyperbolic groups, elliptic and hyperbolic elements. Quasi convex subgroups. Existence of free subgroups using pingpong. Small cancellation groups. Solvability of the word problem and finite presentability. Existence of many quotients.
How does a search engine decide what search results to present? How can a car camera identify automatically whether a pedestrian is crossing? How can we find out which drug is suitable for which patient? How can any application use data from many users to automatically improve user experience? These problems and many more can be solved with modern methods based on machine learning. In this class we will survey diverse practical and successful methods, which can be implemented efficiently and simply, even with very large input sizes. We will understand the mathematical reasoning behind these methods, and apply them in practice on data from real problems. The following topics will be covered:  Supervised learning methods for automatic classification and prediction: Nearest neighbots, SVM, kernels, decision trees, random forests, neural networks, naive Bayes, linear regression.  Unsupervised learning: PCA dimensionality reduction, clustering with kmeans and other methods.  Model fitting methods such as Gaussian mixtures, maximum likelihood, and EM.
Experiments on mechanics: measurement of density and calculation of errors, Impulse and momentum, moment inertia, angular momentum, harmonic oscillations. Experiments on electricity and magnetism: oscilloscope, conductivity, magnetic force. Experiments on waves: wave propagation, Doppler effect, geometrical optics. Heat and fluids: surface tension, thermocouple. All students must perform 1012 experiments and submit report on 5 of them. There will be exam on all the experiments. Sources: Sears F.W., Zemansky M.W., Young H.D., College physics, 7 ed., AddisonWesley,Reading MA 1990; QC 23.S369 1990 Sears F.W.: Optics, 3rd ed., AddisonWesley, Reading MA 1949; QC 355.S45 1949 Halliday D, Resnick R, Krane K S, Physics, vol. 2, 4 ed., Wiley, New York 1992; QC21.2.H355 1992b
Graduate Courses
 Some review of complex variables
 Hilbert spaces of analytic functions. General theory
 Bergman space
 Hardy spaces
 Fractional Hardy space and self similar systems
 BargmannFock space and second quantization
 Banach spaces of analytic functions.
 Frechet spaces and Schwartz spaces
 Proof of Riemann’s mapping theorem
 Dual of the space of functions analytic in a given open set
 Gelfand triples and applications
 Topological manifolds. The fundamental group and covering spaces. Applications.
 Singular homology and applications.
 Smooth manifolds. Differential forms and Stokes’ theorem, definition of deRham cohomology.
 Additional topics as time permits.
 The reflection theorem
 Mostowski collapse theorem
 Absoluteness of formulas
 The constructible universe
 Forcing
 Consistency of the negation of the continuum hypothesis
 Consistency of the negation of the Axiom of choice
 Review of abelian categories and additive functors.
 Differential graded rings, modules and categories.
 The derived category of a differential graded category.
 Derived functors. Resolutions of differential graded modules.
 Review of differentiable manifolds, definition of a Lie group. Quotients in the category of Lie groups, homogeneous manifolds, haar measure, connected components.
 Algebraic groups, matrix groups, the classical groups.
 Lie algebras and connection to Lie groups.
 Nilpotent, solvable and semisimple Lie algebras and Lie groups, Lie theorem, Engel theorem, Levi decomposition.
 CartanKilling form.
 Representation of a Lie algebra over the complex numbers.
 Root and weights, root systems, Dynkin diagrams, classication of complex semisimple Lie algebras.
2022–23–B
Lecturer: Dr. Inna EntovaAizenbud
Hours:  יום ב 14:00  12:00 in בנין 90 (מקיף ז') [90] חדר 136  יום ג 18:00  16:00

Commutative algebra via derived categories (regular and CM rings, Grothendieck’s Local Duality, MGM Equivalence, rigid dualizing complexes).

Geometric derived categories (of sheaves on spaces). Direct and inverse image functors, Grothendieck Duality, Poicar?Verdier Duality, perverse sheaves).

Derived categories associated to nocommutative rings (dualizing complexes, tilting complexes and derived Morita theory).

Derived categories in modern algebraic geometry and modern string theory (a survey).
The field of real numbers $\mathbb{R}$ is defined as the completion of the field of rational numbers $\mathbb{Q}$ with respect to the norm $\·\$. However, there are other norms on $\mathbb{Q}$, each corresponding to a prime number $p$, and the completion of $\mathbb{Q}$ with respect to any such norm leads to the field of $p$adic numbers, denoted by $\mathbb{Q}_p$. This is a topological complete field, and thus it makes sense to develop an analysis on it.
Many features of the $p$adic analysis are very different from familiar ones in real analysis. For example, “the first year calculus dream” of many students comes true: A series converges if and only if its general term goes to $0$. The overall picture of the $p$adic analysis makes impression of a surprising and beautiful one, and easier than its real counterpart. Nowadays, the $p$adic analysis has endless applications in geometry and number theory.
In this course we will study the field of $p$adic numbers from different points of view, stressing similarities to and deviations from the real numbers. If time permits the culmination of the course will be Tate’s thesis (1950), that uses $p$adic analysis to prove the meromorphic continuation of the zetafunction of Riemann and its functional equation.
 Arithmetic of $\mathbb{Q}_p$: sums and products, square roots, finding roots of polynomials.
 Algebraic number theory of $\mathbb{Q}_p$: finite extensions, algebraic closure of $\mathbb{Q}_p$, completion of the algebraic closure, local class field theory will be mentioned.
 Topology of $\mathbb{Q}_p$: elementary topological properties, Euclidean models of $\mathbb{Z}_p$.
 Analysis on $\mathbb{Q}_p$: convergence of sequences and series, radius of convergence, elementary functions $\ln_p$, $\exp_p$, the space of locally constant functions.
 Harmonic analysis on $\mathbb{Q}_p$: characters of $\mathbb{Q}_p$, Haar measure, integration of locally constant functions, Fourier transform.
 The ring of adeles as an object unifying $\mathbb{Q}_p$ for all $p$: topological properties, integration and Fourier transform, Poisson summation formula.
 Tate’s thesis.
Prerequisites: topology, algebraic structures
Topics:

Review of material from past semesters (the courses “Derived Categories I and II”).

Derived categories in commutative algebra: dualizing complexes, Grothendieck’s local duality, MGM Equivalence, rigid dualizing complexes.

Derived categories in algebraic geometry: direct and inverse image functors, global Grothendieck duality, applications to birational geometry (survey), $l$adic cohomology and PoincareVerdier duality (survey), perverse sheaves (survey).

Derived categories in noncommutative ring theory: dualizing complexes, tilting complexes, derived Morita theory.

Derived algebraic geometry: nonabelian derived categories (survey), infinity categories (survey), derived algebraic stacks (survey), applications (survey).
Basics of $C^*$Algebra theory. The spectral theorem for bounded normal operators and the Borel functional calculus. Basic theory of von Neumann algebras. Density theorems, topologies and normal maps, traces, comparison of projections, type classification, examples of factors. Additional topics, for example, noncommutative dynamics, subfactors, group actions, and free probability.
Course Topics:
 Categories and functors: natural transformations, equivalence, adjoint functors, additive functors, exactness.
 Derived functors: projective, injective and flat modules; resolutions, the functors $Ext$ and $Tor$; examples and applications.
 Nonabelian cohomology and its applications.
In this course we will discuss Cantor minimal systems. These are dynamical systems that admit a precise “digital representation” in the sense that the state space can be viewed as an infinite sequence of digits.There has been considerable recent interest in such systems. In this course we will consider formal versions of the question “when are two systems actually the same” . We will introduce and study the notions of isomorphism and orbit equivalence for dynamical systems. The objective of this course is to introduce students to basic notions in dynamical systems through contemporary results. Introduction: Dynamical systems in topological dynamics and ergodic theory, ergodicity and minimality, isomorphism of dynamical systems, the Cantor set, toplogical equivalence relations, orbit equivalence. Invariant measures. Cantor mimimal systems, Bratteli diagrams and the BratteliVershik Model, topological equivalence relations, Orbit cocyles, strong orbit equivalence, invariants for orbit equivalence and topological orbit equivalence.
The aim of the course is to present applications of model theory (a branch of Mathematical Logic) in one or more area of mathematics. The particular direction will be determined by coordination with the students, but might include the following:
 Algebraic theory of differential equations (Galois theory, dimension, classification in dimension 1)
 Model theory of valued fields (imaginaries, integration theory, analytic spaces)
 ominimality, with applications to arithmetic
 Difference fields and difference equations, applications to dynamics, asymptotic theory of the Frobenius
 Continuous model theory, applications to operator algebras, probability etc. Background from logic and other relevant areas will be covered as necessary.
 Background material in functional analysis and Hilbert space theory; basics of Banach algebras, and Gelfand theory.
 Basic results: commutative $C^*$algebras, positivity, ideals and quotients, states and representations, the GNS constuction, the algebra of compact operators.
 A discussion of some basic examples in the theory (e.g. AF algebras, Toeplitz and Cuntz algebras, irrational rotation algebras, as time permits). Introduction to Ktheory.
Further topics as time permits.
 Measure preserving systems for Borel group actions
 Ergodicity, ergodic decomposition, mixing and weak mixing
 Minimality and unique ergodicity
 Mean and pointwise ergodic theorems for a single transformation
 (*) Joinings
 (*) Decomposition of a measure relative to a partition and conditional measures
 (*) Entropy
 (*) Ergodic theorems for general groups and amenability.
(*) denotes optional topics to be covered according to class level and teacher discretion.

Basic Algebraic Structures: rings, modules, algebras, the center, idempotents, group rings

Division Rings: the Hamiltonian quaternions, generalized quaternion algebras, division algebras over $\mathbb{F}_q$, $\mathbb{C}$, $\mathbb{R}$, $\mathbb{Q}$ (theorems of Frobenius and Wedderburn), cyclic algebras, the Brauer–Cartan–Hua theorem

Simplicity and semisimplicity: simplicity of algebraic structures, semisimple modules, semisimple rings, Maschke’s theorem

The Wedderburn–Artin Theory: homomorphisms and direct sums, Schur’s lemma, the Wedderburn–Artin structure theorem, Artinian rings

Introduction to Group Representations: representations and characters, applications of the Wedderburn–Artin theory, orthogonality relations, dimensions of irreducible representations, Burnside’s theorem

Tensor Products: tensor products of modules and algebras, scalar extensions, the Schur index, simplicity and center of tensor products, the Brauer group, the Skolem–Noether theorem, the double centralizer theorem, maximal fields in algebras, reduced norm and trace, crossed products
2022–23–B
Lecturer: Prof. Eitan Sayag
Hours:  יום ב 12:00  10:00 in בנין 90 (מקיף ז') [90] חדר 238  יום ד 12:00  10:00
 Cohomology: definitions, Universal coefficient theorem, Orientation, Poincare duality, cap and cup products, cohomology ring, Kunneth formula
 Review of (Smooth manifolds, differential forms, orientability, Stokes theorem), degree of the map, Sard theorem, DeRham cohomology.
 Isomorphism between DeRham cohomology and singular cohomology.
 (If time permits) additional topics according to the instructor preferences
 Riemann surfaces
 Holomorphic functions of several variables
 Isolated singularities of holomorphic functions
 Fundamentals of differential topology
 Topology of singularities.
 Sheaves on topological spaces
 Affine schemes
 Schemes and morphisms between them.
 Quasicoherent sheaves
 Separated and proper morphisms.
 Vector bundles and the Picard group of a scheme.
 The functor of points and moduli spaces.
 Morphisms to projective space and blowups.
 Smooth morphisms and differential forms.
 Sheaf cohomology.
 Group schemes.
Banach spaces and Hilbert spaces. Basic properties of Hilbert spaces. Topological vector spaces. BanachSteinhaus theorem; open mapping theorem and closed graph theorem. HahnBanach theorem. Duality. Measures on locally compact spaces; the dual of $C(X)$. Weak and weak$*$ topologies; BanachAlaoglu theorem. Convexity and the KreinMilman theorem. The StoneWeierstrass theorem. Compact operators on Hilbert space. Introduction to Banach algebras and Gelfand theory. Additional topics as time permits.
 vector bundles and Kgroups of topological spaces
 Bott’s Periodicity theorem and applications to division algebras
 Index of Fredholm operators and Ktheory
 If time permits: smooth manifolds, DeRham cohomology, Chern classes, Elliptic operators, formulation of AtiyahSinger index theorem, relations to GaussBonnet theorem
In a random process, by definition, it is not possible to deterministically predict the next step. However, we will see in this course how to predict rigorously the long term behavior of processes. We will study in this course processes, known as Markov processes, in which the next step depends only on the current position. We will see that these processes are deeply related to electrical networks, and to notions from information theory such as entropy. We will develop techniques of discrete analysis, which are counterparts of classical analysis in the discrete setting. These notions are at the cutting edge of current research methods in these fields
 Affine algebraic sets and varieties.
 Local properties of plane curves.
 Projective varieties and projective plane curves.
 Riemann–Roch theorem.
 Fundamental theorems and basic definitions: Convex sets, separation , Helly’s theorem, fractional Helly, Radon’s theorem, Caratheodory’s theorem, centerpoint theorem. Tverberg’s theorem. Planar graphs. Koebe’s Theorem.
 Geometric graphs: the crossing lemma. Application of crossing lemma to Erdos problems: Geometric Incidences, Repeated distance problem, distinct distances problem. Selection lemmas. Counting $k$sets. An application of incidences to additive number theory.
 Coloring and hiting problems for geometric hypergraphs : $VC$dimension, Transversals and Epsilonnets. Weak epsnets for convex sets. $(p,q)$Theorem, Conflictfree colorings.
 Arrangements : Davenport Schinzel sequences and sub structures in arrangements.
 Geometric Ramsey and Turan type theorems: Application of Dilworth theorem, ErdosSzekeres theorem for convex sets, quasiplanar graphs.
The course will present the fundamentals of Shelah’s pcf theory and some of its many applications in cardinal arithmetic, infinite combinatorics, general topology and Boolean algebra.
Pcf theory deals with the spectrum of possible cofinalities of reduced products of small sets of regular cardinals. The pcf theorem asserts the existence of a sequence of generators for the set of possible cofinalities.
The most well known application of the theory are Shelah’s bound on the powers of strong limit singular cardinals, most notably on the power of the first singular cardinal, which actually follows from the more informative bound on the covering number of countable sets. These results will be presented in full. Other applications which will be given are the construction of topological spaces, embeddability of Boolean algebras, extensions of measures and bounds on coloring numbers of graphs.
The general question that leads the course is: what can we deduce about a group when studying random walks on it.
Stationary dynamics is a branch of Ergodic theory that focuses on measurable group actions arising from random walks. The main object studied is the FurstenbergPoisson boundary.
Applications of the theory can be found in rigidity theory, and recently connections with operator theory have been established
Topics:
 Brief introduction to Ergodic theory: Borel spaces, factors, compact models. Probability measure preserving actions and measure class preserving actions. Stationary measures.
 Random Walks: Markov chains, Martingale convergence theorem, Random walks on groups, FurstenbergPoisson boundary. ChoquetDeny theorem, amenable groups. Entropy. Realization of the Furstenberg Poisson boundary.
 Applications to Rigidity: Margulis’ Normal Subgroup Theorem, and BaderShalom’s theorem (IRS rigidity).
This is a course on algebraic varieties over an algebraically closed field. Technically it is much easier than a course on schemes, but it does not touch upon arithmetic geometry. We shall cover most of the standard material, with some additional glances into more advanced or specialized topics. The topics listed below will be adapted, to some extent, to the background and capabilities of the registered students.
The course will span two semesters (2 hours a week each semester). The list of topics below is for both parts of the course. Course Topics: (For both semesters, as much as time permits. Anticipated range: until topic 10.)
 Categories and functors. Either a review (if the students are prepared), or a systematic but gradual study, mixed in with the other topics.
 Ringed spaces. Topological spaces equipped with sheaves of rings of functions. Maps between ringed spaces. Study familiar examples: topological spaces and differentiable manifolds.
 Recalling commutative algebra. Noetherian rings, Hilbert Basis Theorem, algebraic and transcendental field extensions, Hilbert Nullstellensatz. Localization of rings. Prime ideals. Tensor products of rings. (Students are expected to know this material.)
 Affine algebraic varieties. (All varieties are over an algebraically closed field.) The Zariski topology and the sheaf of algebraic functions. Maps of affine varieties, fibered products, subvarieties, local rings. Dimension of an affine variety and its function field.
 Projective algebraic varieties. Definition by homogeneous coordinate rings and by gluing. Fibered products of projective varieties. Quasiprojective varieties.
 Algebraic varieties. The separation condition; varieties that are not quasiprojective.
 Types of maps between varieties. Finite, quasifinite, flat, affine and projective maps.
 The classification of curves. Nonsingular projective curves and their functions fields. Elliptic curves (brief).
 Enumerative geometry. Bezout Theorem. Some intersection theory. A brief survey of modern methods.
 Vector bundles. Definition, examples. Gluing vector bundles.
 Line bundles and the Picard group. Ample line bundles and morphisms to projective space. The automorphism group of projective space.
 Birational geometry. Blowups. Some examples. Singularities and their resolutions. A brief survey of modern methods.
 Sheaves of modules. From locally free sheaves to vector bundles and back. Coherent sheaves. Serre’s Theorem on affine open sets.
 Sheaf cohomology (brief). RiemannRoch Theorem for curves. A quick tour of Serre Duality.
 Differential algebraic geometry (brief). The tangent bundle, derivations, differential forms. Etale and smooth maps.
 Algebraic groups (brief). Some examples. Lie algebras. Group actions, quotients.
This course will cover a number of fundamentals of model theory including:
 Quantifer Elimination
 Applications to algebra including algebraically closed fields and real closed fields.
 Types and saturated models.
Given time, the course may also touch upon the following topics:
 Vaught’s conjecture and Morley’s analysis of countable models
 $\omega$stable theories and Morley rank
 Fraisse’s amalgamation theorem.
Prerequisites
Students should be familiar with the following concepts: Languages, structures, formulas, theories, Godel’s completeness theorem and the compactness theorem.
 Doubly periodic functions. Lattices, complex tori. The field of elliptic functions.
 Riemann surfaces: definitions, maps, the genus, Riemann–Hurwitz formula.
 Differential forms on a Riemann surface. The Abel–Jacobi map.
 Local study of holomorphic functions. Points of Riemann surfaces as valuations on the field of meromorphic functions. Classifications of the absolute values on the field of rational numbers. The field of padic numbers. The product formula.
 Algebraic curves over a field. Algebraic curves over C and relation to Riemann surfaces.
 Rational points. Arithmetic of curves according to the genus. The case of conics (genus 0). Hasse principle. Finite generation of the rational points on elliptic curves (genus 1): the case of Fermat quartic.
 Modular surfaces and modular forms. Analytic construction of rational points on elliptic curves.
 Expander graphs and their applications — Equivalent definitions, Mixing Lemma, Alon–Boppana Theorem, Applications, Constructions
 Kazhdan Property (T)  group representations (short intro.), Cayley and Schrier graphs, Property (T) — definition and properties, examples, construction of expander graphs via property (T)
 Additional topics (as time allows)
 Sheaves on topological spaces
 Affine schemes
 Schemes and morphisms between them.
 Quasicoherent sheaves
 Separated and proper morphisms.
 Vector bundles and the Picard group of a scheme.
 The functor of points and moduli spaces.
 Morphisms to projective space and blowups.
 Smooth morphisms and differential forms.
 Sheaf cohomology.
 Group schemes.
This is a first course in modern commutative algebra that provides the background for further study of commutative and homological algebra, algebraic geometry, etc.
Syllabus
 Rings, ideals, and homomorphisms
 Modules, CayleyHamilton theorem, and Nakayama’s lemma
 Noetherian rings and modules, Hilbert basis theorem
 Integral extensions, Noether normalization lemma, and Nullstellensatz
 Affine varieties
 Localization of rings and modules
 Primary decomposition theorem
 Discrete valuation rings
 Selected topics
Literature
 Miles Reid, Undergraduate Commutative Algebra
 Miles Reid, Undergraduate Algebraic Geometry
 Altman, Kleiman, A Term of Commutative Algebra
The course will discuss arithmetic, analysis and geometry over $p$adic fields. The primary aim is to provide the necessary background for Dwork’s rationality theorem of Weil Zeta functions and to further developments in the subject.
In the first part of the course we will cover some classical applications of $p$adic numbers to elementary number theory including the local to global principle for quadratic forms.
In the second part we will study analysis: $p$adic functions and $p$adic power series and present Dwork’s proof.
Topics:

Rigidity, residues and duality over commutative rings. We will study rigid residue complexes. We will prove their uniqueness and existence, the trace and localization functoriality, and the indrigid trace homomorphism.

Derived categories in geometry. This topic concerns geometry in the wide sense. We will prove existence of Kflat and Kinjective resolutions, and talk about derived direct and inverse image functors.

Rigidity, residues and duality over schemes. The goal is to present an accessible approach to global Grothendieck duality for proper maps of schemes. This approach is based on rigid residue complexes and the indrigid trace. We will indicate a generalization of this approach to DM stacks.

Derived categories in noncommutative ring theory. Subtopics: dualizing complexes, tilting complexes, the derived Picard group, derived Morita theory, survey of noncommutative and derived algebraic geometry.
Aims of the course

to introduce students to the new algebraic structure of Lie algebras, to teach them to recognize examples and to see deep consequences achieved using mostly the technique of linear algebra.

Representation theory is about understanding and exploiting symmetry using linear algebra. The students will study basics of representation theory, that are common for representations of associative algebras or groups. They will see how representation theory is used for the classification of simple Lie algebras.

After taking this course the students will be much better prepared to study Lie groups, or representation theory of groups. Root systems, studied in the course, which is a combinatorial object, and associated Coxeter groups often appear in geometric group theory, as well as in singularity theory.

The students will practice both proving statements and doing explicit computations in matrix algebras, that are the main source of Lie algebras

Motivated students will have a chance to present a topic from the course before their peers, which is an instructive task.

The course is useful for the graduate students in Physics. Their participation is an excellent opportunity for the students of two departments to meet and exchange their views on the topic.
Course topics
 Basic concepts and examples
 Connection to Lie groups.
 Nilpotent and solvable algebras
 Killing form and semisimplicity
 Weyl’s theorem
 Root systems
 Classification of simple Lie algebras
 Classification of irreducible representations of simple Lie algebra
 Additional topics (time permitted)
Generally speaking, algebraic geometry deals with geometric objects defined by polynomial equations. We will cover topics 13 below, and maybe one other topic, depending on the level of the students.
Topics
 Topics from commutative algebra.
 Algebraic varieties over an algebraically closed field: definitions and main properties.
 Curves and their function fields.
 Intersection Theory in the projective plane.
 Vector bundles and coherent sheaves.
 Picard group and projective embeddings.
 Schemes: definitions, examples and basic properties.
2022–23–B
Introduction to Algebraic Geometry
See course syllabus and administrative information on the course web page. These pdf files are also uploaded here.
My active web page is this (on Google Drive), not the one you see when you click my name above.
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This course concerns the physical notion of phase transition, specifically in the model known as “percolation”.
We will review the main mathematical results regarding percolation and its related counterparts the Ising model, Potts model and FortuinKasteleyn cluster model, starting from works of Ising and Pierles in the beginning of the 20th century and culminating in modern work of Smirnov (for which he was awarded a Fields Medal).
The topics in the course are, time permitting:
 Percolation on graphs. Definitions and basic properties.
 Harris’ inequality
 van den BergKesten inequality, Reimer’s inequality
 Russo’s formula
 BurtonKeane Theorem
 Exponential decay of correlations in subcritical regime
 Planar percolation: RussoSeymourWelsh theory
 Planar percolation: the HarrisKesten theorem.
 Conformal invariance: CardySmirnov formula on the triangular lattice
 Percolation on groups
 Critical percolation on nonamenable groups: BLPS
Banach spaces and Hilbert spaces. Basic properties of Hilbert spaces. Topological vector spaces. BanachSteinhaus theorem; open mapping theorem and closed graph theorem. HahnBanach theorem. Duality. Measures on locally compact spaces; the dual of $C(X)$. Weak and weak$*$ topologies; BanachAlaoglu theorem. Convexity and the KreinMilman theorem. The StoneWeierstrass theorem. Banach algebras. Spectrum of a Banach algebra element. Gelfand theory of commutative Banach algebras. The spectral theorem for normal operators (in the continuous functional calculus form).
Course Topics
 Modules: free modules, exact sequences, tensor products, Hom modules, flatness.
 Prime ideals and localization: local rings, Nakayama’s Lemma, the spectrum of a ring, dimension and connectedness.
 Noetherian rings: the Hilbert basis theorem, the ArtinRees lemma, completion, grading.
 Dimension theory: the Hilbert nullstellensatz, Noether normalization, transcendence degree.
Examples: time flow for solutions of differential equations, symbolic dynamics, cellular automata, geodesic and horocyclic flows, interval exchange transformations, Chabauty space, profinite actions. Basic concepts: Factors and extensions, topological transitivity, minimality, equicontinutity, distality, proximality, weak mixing, almost 11 extenstions. Topological entropy. The Ellis semgroup, Ellis theorem on the existence of idempotents, EllisAuslander theorem, Hindeman?s theorem. Universal constructions. Stone Cech compactification. The universal ambit and the universal minimal flow. Universal proximal and strongly proximal flows. Furstenberg boundary.
Definitions of expander graphs, CheegerBuser inequality, Expander mixing lemma, AlonBoppna Theorem, Existence of expander graphs (non constructive proof), Application of expander graphs to errorcorrecting codes, Groups – basic concepts (actions, Cayley graphs, normal subgroups, unitary representations), Margulis expanders, Kazhdan property (T) – definition, hereditary properties, relation to expander graphs, other constructions of expander graphs.
 Lattices. Doubly periodic functions.
 Riemann surfaces: definition and examples (Riemann sphere, roots, logarithms).
 Riemann surfaces: Euler characteristic, genus, Hurwitz formula. Riemann–Roch theorem (statement)
 Complex tori and elliptic curves.
 Morphisms between elliptic curves. The group law. Arithmetic of elliptic curves (a glimpse: Mordell’s theorem, Lfunctions, the Birch and SwinnertonDyer conjecture).
 The group SL(2,Z). The space of all lattices. The trefoil knot.
 Modular forms for SL(2,Z): finitedimensionality. Eisenstein series.
 Theta series. Four squares. Even unimodular lattices and sphere packing in dimension 8.
 Hecke operators. `Reminders’ on the Riemann zeta function. Lfunctions. Modularity of elliptic curves (statement).
 The Ramanujan conjecture. Expander graphs. The jinvariant. Moonshine.
 Modular curves and their compactification.
 Complex multiplication.
 Normal families and rational maps.
 The Fatou and Julia sets.
 Properties of the Julia set.
 The structure of the Fatou set.
 Periodic points.
 Forward invariant components.
 The no wandering domains theorem.
 Commuting and semiconjugate rational functions.
 Introduction to arithmetic dynamics.
The course will examine the role of the representation theory of the group G=SL(2) in the mathematics of 20th century. We plan to achieve this by surveying its connections to geometry, algebra, analysis and number theory.
Permutation representation and the Sylow theorems; Representations of groups on groups, solvable groups, nilpotent groups, semidirect and central products; Permutation groups, the symmetric and alternating groups; The generalized Fitting subgroup of a finite group; pgroups; Extension of groups: The first and second cohomology and applications.
Deprecated Courses
 Real numbers (axiomatic theory). Supremum and Infimum of a set. Existence of an nth root for any a > 0. 2. Convergent sequences, subsequences, Cauchy sequences. The BolzanoWeierstrass theorem. Upper and lower limits. 3. Series. Partial sums, convergent and divergent series, Cauchy criterion. Series of nonnegative terms. The root and the ratio tests. Series of arbitrary terms. Dirichlet, Leibnitz, and Abel theorems. Rearrangements of series. The Riemann Theorem. 4. The limit of a function. Continuous functions. Continuity of the elementary functions. Properties of functions continuous on a closed interval. Uniformly continuous functions. Cantor?s theorem. 5. The derivative of a function. Mean value theorems. Derivatives of higher order. L’Hospital’s rule. Taylor’s theorem.
 The Riemann integral: Riemann sums, the fundamental theorem of calculus. Methods for computing integrals (integration by parts, substitution, partial fractions). Improper integrals and application to series. Numerical integration. Stirling’s formula and additional applications time permitting.
 Uniform and pointwise convergence. Cauchy’s criterion and the Weierstrass Mtest. Power series. Taylor series, analytic and nonanalytic functions. Convolutions, approximate identities and the Weierstrass approximation theorem. Additional applications time permitting.
 A review of vectors in R^n and linear maps. The Euclidean norm and the CauchySchwarz inequality. Basic topological notions in R^n. Continuous functions of several variables. Curves in R^n, arclength. Partial and directional derivatives, differentiability and C^1 functions. The chain rule. The gradient. Implicit functions and Lagrange multipliers. Extrema in bounded domains.
 Basic concepts of topology of metric spaces: open and closed sets, connectedness, compactness, completeness.
 Normed spaces and inner product spaces. All norms on $\mathbb{R}^n$ are equivalent.
 Theorem on existence of a unique fixed point for a contraction mapping on a complete metric space.
 Differentiability of a map between Euclidean spaces. Partial derivatives. Gradient. Chain rule. Multivariable Taylor expansion.
 Open mapping theorem and implicit function theorem. Lagrange multipliers. Maxima and minima problems.
 Riemann integral. Subsets of zero measure and the Lebesgue integrability criterion. Jordan content.
 Fubini theorem. Jacobian and the change of variables formula.
 Path integrals. Closed and exact forms. Green’s theorem.
 Time permitting, surface integrals, Stokes’s theorem, Gauss’ theorem
 Normed spaces and spaces with inner products. The projection theorem for finite dimensional subspaces. Orthogonal systems in infinite dimensional spaces. The Bessel inequality and the Parseval equality, closed orthogonal systems. The Haar system.
 The Fourier series (in real and complex form). Approximate identities, closedness of the trigonometric / exponential system. Uniform convergence of the Fourier series of piecewise continuously differentiable functions on closed intervals of continuity; the Gibbs phenomenon. Integrability and differentiability term by term.
 The Fourier transform. The convolution theorem. The Plancherel equality. The inversion theorem. Applications: low pass filters and Shannon’s theorem.
 The Laplace transform. Basic relations and connection with the Fourier transform. A table of the Laplace transforms. The convolution integral. Application of the Laplace transform for solution of ODEs.
 Introduction to the theory of distributions. Differentiation of distributions, the delta function and its derivatives. Fourier series, Fourier transforms, and Laplace transforms of distributions.
Ordinary differential equations of first order, existence and uniqueness theorems, linear equations of order n and the Wronskian, vector fields and autonomous equations, systems of linear differential equations, nonlinear systems of differential equations and stability near equilibrium
2022–23–B
Lecturer: Prof. Dmitry Kerner
Hours:  יום ב 16:00  14:00 in קרייטמןזלוטובסקי(חדש) [34] חדר 103  יום ה 14:00  12:00 in בנין 90 (מקיף ז') [90] חדר 229
State space and transfer functions, stabilitystate space and input output, observability and controllability, realization theory, Hankel Matrices, statefeedback ans stabilization, Ricatti equations.
The aim of the course is to train students in creative problem solving in various areas of mathematics, in a manner simulating, to some extent, mathematical research (rather than ordinary courses, where homework assignments are, usually closely ? and quite obviously  related to the topics reviewed in class). The course will focus on problems whose solutions call for tools and ideas from several fields of mathematics, and will reveal ? on a miniature scale  the beauty of mathematics as an integral field of knowledge, where between seemingly unrelated subjects deep and surprising connections may arise. The problems will focus on topics in classical and modern mathematics that, due mostly to their interdisciplinary nature, are not ? as a rule ? covered in the core classes offered by the department (such topics as the axiom of choice and its applications, the BanachTarski paradox, transcendental number theory etc.). Classes will be divided between lectures, given by the instructor, filling in background material required to address the problems in question, and between students’ presentation of their solutions to the work sheets distributed previously. In addition to all of the above, the course will help students improve their skills in searching the mathematical literature and in the art of writing and presenting proofs.
The process diffusion limited aggregation, or DLA, is a remarkable process stemming from physics. Although it is known for almost 40 years, it still is mysterious and difficult to mathematically understand. Classically it has been studied in the plane. For this project we will simulate the process in other geometries, Euclidean as well as nonEuclidean. The goal is to obtain largescale simulations, which may lead to further understanding and new ideas. Further, we will use the simulations repeatedly to measure experimentally different quantities associated with DLA, such as growth rate, fractal dimension, etc.
Probability theory: discrete and continuous variables, independent vs dependent variables, six basic discrete distributions: Bernoulli, binomial, uniform, geometric, negative binomial, Poissonian. Mean, variance, moments, probability generating function. Five basic continuous distributions: uniform, normal, exponential, gamma, beta. Moment generating function. Events, conditional probability, aging of molecules, entropy and related concepts, scores and support. Generating various probabilities. Many random variables. EST library. Covariance and correlation, iid, minimum and maximum of many random variables.Theoretical statistics; random sampling. Classical vs Bayesian approach. Distributions of the mean and variance of the sample; methods of calculating point estimates; point estimator for the mean; point estimator for the variance; biased vs unbiased, MSE, confidence intervals for the parameters of distribution. Basic ideas and definitions for the test of the hypothesis; errors of type I and II; Pvalues, tests for mean values, variances and proportions; test for the goodness of fit; test of independence; correlation coefficient; and its tests; linear regression; Likelihood ratios, information and support; maximum value as test statistic. Nonparametric: MannWhitney and permutation tests.Bayesian approach to hypothesis testing and estimation.ANOVA  analysis of variance: oneway and twoway.More theory on classical estimation: optimality aspects.BLAST.
The course discusses elements of linear algebra over the real and complex numbers. 1. The complex numbers. Linear equations: elementary operations, row reduction, homogeneous and nonhomogeneous linear equations. 2. Real and complex vector spaces. Examples, subspaces, linear dependence, bases, dimension. 3. Matrix algebra. Matrix addition and multiplication, elementary operations, LU decomposition, inverse matrix, the determinant, Cramer’s law. 4. Linear transformations: examples, kernel and image, matrix representation. 5. Inner products, orthonormal sequences, the GramSchmidt process. 6. Diagonalization: eigenvalues and eigenvectors, the characteristic polynomial. Time permitting: unitary and orthogonal matrices and diagonalization of Hermitian and symmetric matrices.
First order equations: separable equations, exact equations, linear equations, Bernulli equations. Existence and uniqueness. Second order equations. Reduction of order. Linear homogeneous equations, fundamental solutions and Wronskian. Inhomogeneous equations, variation of parameters. Equations with constant coefficients and the method of undetermined coefficients. Linear equations of higher order. Euler equations. Systems of differential equations. Laplace transform and its properties. Solution of initial value problems with discontinuous forcing functions. Impulse functions. Convolution.
 Fields: definitions, the field of complex numbers.
 Linear equations: elementary operations, row reduction, homogeneous and inhomogeneous systems, representations of the solutions.
 Vector spaces: examples, subspaces, linear dependence, bases, dimension.
 Matrix algebra: matrix addition and multiplication, elemetary operations, the inverse of a matrix, the determinant, Cramer’s rule.
 Linear transformations: examples, kernel and image, matrix representation.
 Diagonalization: eigenvectors and eigenvalues, the characteristic polynomial, applications.
 Bilinear forms.
 Finite dimensional inner product spaces.
 Operators on finite dimensional inner product spaces: the adjoint, self adjoint operators, normal operators, diagonalization of normal operators.
The course provides a basic introduction to “naive” set theory, propositional logic and predicate logic. The course studies many important concepts which serve as the building blocks for any mathematical theory An emphasis is given to clear proof writing and correct usage of the mathematical language.A. Basics of Set Theory1. The notion of a set. Set operations: union, intersection, difference, complementation, and the power set.2. Cartesian products and binary relations. Operations on relations.3. Functions: domain and range, one to one and onto. Composition.4. Equivalence relations and set partitions.5. Order relations: partial, linear and wellfounded.6. Induction theorems: mathematical, complete and wellfounded.B. Set Cardinality1. The notion of cardinality. Finite, infinite and countable sets.2. Cantor’s theorem.3. The cardinality of the set of real numbers and other sets.C. Propositional Calculus1. The language of propositional calculus. Logical connectives.2. Logical implication and logical equivalence of propositional formulas.3. Disjunctive normal form.4. Complete sets of logical connectives.D. Predicate Calculus (First Order Logic)1. The language of predicate calculus: terms, formulas, sentences.2. Structures, assignments for a given structure.3. Logical implication and logical equivalence of first order formulas.4. Elementary equivalence of structures, definable sets in a given structure.
Logic and Set theory: propositional calculus, boolean operators and their truth tables, truth values of formulae, logical equivalence and logical inference, tautologies and contradictions, the important tautologies, e.g., the distributive laws and De Morgan formulae. Sets: inclusion, union, intersection, difference, cartesian product, relations, equivalence relations, partial orders, linear orders, and functions. Basic Combinatorics: induction, basic counting arguments, binomial coefficients, inclusionexclusion, recursion and, generating functions. Graphs: general notions and examples, isomorphism, connectivity, Euler graphs, trees.
Notes
 Courses marked with (*) are required for admission to the M.Sc. program in Mathematics.
 The M.Sc. degree requires the successful completion of at least 2 courses marked (#). See the graduate program for details
 The graduate courses are open to strong undergraduate students who have a grade average of 85 or above and who have obtained permission from the instructors and the head of the teaching committee.
 Please see the detailed undergraduate and graduate programs for the for details on the requirments and possibilities for complete the degree.