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.
A glimpse about theoretical guarantees, such as upper bounds on the generalization error, are provided at the end of the lecture.
A prerequisite for this course is Machine Learning I.
Langue du cours : Français ou Anglais, slides en Anglais
Credits ECTS : 4
- Teaching coordinator: Karim Lounici