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.
Proofs will not be made during the lectures: some will be made during the labs others could be found in a document. Finally, each lab will finish with a numerical lab in Python
Validation
- A Kaggle challenge (10 points)
- A final exam (10 points)
- Teaching coordinator: Bianchi Pascal
- Teaching coordinator: El Mhamdi El Mahdi
- Teaching coordinator: Klein Thierry
- Teaching coordinator: Le Pennec Erwan
- Teaching coordinator: Philippenko Constantin