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\begin{document}
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\begin{center}

  \huge{The Department of Mathematics}\\[0.1\baselineskip]
  \Large{2018--19--B term}\\[0.2\baselineskip]

\end{center}

\begin{description}
  \item[Course Name]
    Statistical methods for big data

  \item[Course Number]
    \LRE{201‭.1‭.9131}

  \item[Course web page]\mbox{}\\
    \url{https://www.math.bgu.ac.il//en/teaching/spring2019/courses/statistical-methods-for-big-data}

  \item[Lecturer]
    Dr. Luba Sapir,
    \nolinkurl{<lsapir@post.bgu.ac.il>},
    Office 109-

\item[Office Hours]
  \url{https://www.math.bgu.ac.il/en/teaching/hours}
\end{description}

\section*{Abstract}




\section*{Requirements and grading\footnote{Information may change during the first two weeks of the term. Please consult the webpage for updates}}






\section*{Course topics}

\begin{enumerate}
\item{} Review of probability:
a.	Basic notions.
b.	Random variables, Transformation of random variables, Independence.
c.	Expectation, Variance, Co-variance. Conditional Expectation.
\item{} Probability inequalities:  Mean estimation, Hoeffding?s inequality.
\item{} Convergence of random variables:
a.	Types of convergence.
b.	The law of large numbers.
c.	The central limit theorem.
\item{} Statistical inference:
a.	Introduction.
b.	Parametric and non-parametric models.
c.	Point estimation, confidence interval and hypothesis testing.
\item{} 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
\item{} Parametric interval estimation
a.	Introduction.
b.	Pivotal Quantity.
c.	Sampling from the normal distribution: confidence interval for mean, variance.
d.	Large-sample confidence intervals.
\item{} Hypothesis testing concepts: parametric vs.  nonparametric
a.	Introduction and main definitions.
b.	Sampling from the Normal distribution.
c.	p-values.
d.	Chi-square distribution and tests.
e.	Goodness-of-fit tests.
f.	Tests of independence.
g.	Empirical cumulative distribution function. Kolmogorov-Smirnov Goodness-of fit test.
\item{} Regression.
a.	Simple linear regression.
b.	Least Squares and Maximum Likelihood.
c.	Properties of least Squares estimators.
d.	Prediction.
\item{} Handling noisy data, outliers.
\end{enumerate}

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\end{document}

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