Descriptif

This course will introduce the theoretical and practical aspects of Statistical Machine Learning (ML) for the uninitiated. The course will cover the fundamental concepts of ML theory necessary to understand and implement ML algorithms and evaluate their performance on real problems. Through lectures, you will be presented with theoretical objects and notions, as well as presented with classical algorithms for classification, regression, and time series problems, including classical methods, kernel methods, and deep learning. Through IT practicals, you will be introduced to methodological keystones for using ML in the real world, including developing specialised coding skills and best practices. The course presumes familiarity with basic statistics notions and basic coding skills in Python.