Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn from data. A major focus of machine learning is to automatically learn complex patterns and to make intelligent decisions based on them. The set of possible data inputs that feed a learning task can be very large and diverse, which makes modelling and prior assumptions critical problems for the design of relevant algorithms.
This course focuses on the methodology underlying supervised and unsupervised learning, with a particular emphasis on the mathematical formulation of algorithms, and the way they can be implemented and used in practice. We will therefore describe some necessary tools from optimization theory, and explain how to use them for machine learning. Numerical illustrations will be given for most of the studied methods.
We will follow the book from Hastie, Tibshirani and Friedman called "Elements of Statistical Learning". This will define the structure of the course even if we will sometime complement the book during the lectures
- Overview of Supervised Learning (Chap 1, new)
- Linear method for regression (Chap 2, compl.)
- Linear Methods for Classification (Chap3, compl.), Kernel Smoothing Methods (Chap 6, compl.)
- Model Assessment and Selection (Chap 7, new)
- Trees (Chap 9, new), Boosting (Chap 10, new)
- Averaging (Chap 8, new), Random Forests (Chap 15, new), Ensemble Methods Chap 16, new)
- Neural networks (Chap 11, new)
- Support Vector Machines (Chap 12, compl.)
Language : English