Skip to page content

Courses

  • Statistical Inference (40005)
    • תקציר הקורס:

    Abstract:

    Descriptive statistics, estimation, properties of estimators and estimation methods, Confidence intervals, testing of statistical hypothesis, tests about the expectation, proportion and the variance of a population, goodness of fit and independence tests,

    simple linear regression, non-parametric statistics. Using the R software.
  • The Statistical Aspects Of Machine Learning (40242)
    • תקציר הקורס:

    Abstract:

    In this course we will look at machine learning from a probabilistic point of view.

    The basic assumption is that the data comes from some distribution (sometimes unknown).

    We will study Bayesian statistical methods for estimating unknown parameters of different distributions, we will use approximation methods such as MCMC to estimate densities, we will look at machine learning from the point of view of Decision Theory and we will discuss some classification problems.

    We will study clustering methods and the EM algorithm for GMM and in general.

    Other optional topics are probabilistic models such as Bayesian networks and Markov chains. The studied material will be applied using R software.