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,

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