Machine Learning Track

Objective

It will allow one to study methods of unsupervised, semi-supervised and supervised learning and visualization methods. These ones are commonly used in the context of data mining and knowledge discovery in large and high-dimensional data sets. The data can come from various fields such as genomics, text analysis, web usage mining, marketing, image and speech processing. Further, this formation will also help to handle the use of probabilistic models such as the finite mixtures models which have become a standard tool in machine learning.

Most crucial scientific, sociological, political, economic, bioinformatics, and business decisions are made based on data analysis. Often data are available in abundance, but they are of little help unless they are summarized and an appropriate interpretation of the summary quantities made. However, such a summary and corresponding interpretation can rarely be made just by looking at the raw data. A careful scientific scrutiny analysis of these data can usually provide an enormous amount of valuable information. Often such an analysis may not be obtained just by computing averages, standard deviations or plotting histograms, box or scatter plots. Then we need multivariate analysis and adapted graphics.

The instruction language is English.

Core Courses and Teachers (common to ABC and ML)

 

Specific-Track courses and Teachers