Course info

• DATA832
• INFO831

This module is a continuation of the DATA832 and INFO831 modules in which the BA-ba of data science has been presented through the different paradigms of machine learning and exploratory data analysis. Experimental studies with basic algorithmic machinery have highlighted limitations of basic modeling tools and the need of using advanced methods.

A set of advanced methods, extending the fundamentals of machine learning, is presented in this module. Each approach improves the learning process by focusing on a particular aspect, such as reducing variance of decisions, dealing with non-linear problems, or learning from a very large number of examples with automatic feature extraction.

A conceptual presentation of different methods will be associated with some thoughts on their implementation and with experimentations based on case studies used in applied research.

Contents

• ensemble methods (bagging, random forests, boosting)
• vector support machines, kernel methods
• deep learning
• renforcement methods
• time series, sequential patterns