The course covers statistical techniques and tools such as kernel methods for estimating the density and regression functions, machine learning, hidden Markov Chain, Expectation-Maximization algorithm, classification, cluster analysis and support vector machines for analysing large data sets and for searching for unexpected relationships in the data. It also covers model selection for searching through a large collection of potential local models that describe some aspects of the data in an easily understandable way.

CILO-1: Use kernel methods for density estimation and regression functions in data analysis. CILO-2: Evaluate and interpret the behaviour of advanced machine learning techniques by using appropriate strategies. CILO-3: Use principal component analysis, cluster analysis and support vector machines in data analysis. CILO-4: Apply the Expectation-Maximization algorithm to analyze large scale data sets.