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.
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. A glimpse about theoretical guarantees, such as upper bounds on the generalization error, are provided during the last lecture.
The methodology will be the main concern of the lectures while some proofs will be done during the PCs. Practice will be done through a challenge.