1. Introduction;

2. Model Representation, Linear Regression;

3. Logistic Regresion;

4. Dimensionality Reduction;

5. Neural Networks;

6. Unsupervised and Self-Supervised Learning;

7. Density Estimation;

8. Reinforcement Learning;

9. Physics-Inspired Machine Learning;

- C. Bishop. Pattern Recognition and Machine Learning, Springer, ISBN-13: 978-0387310732, 2011.

- M. Mohri, A. Rostamizadeh, A. Talwalkar, F. Bach. Foundations of Machine Learning, ISBN-13: 978-0262039406, 2018.

- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and 80% of the practical classes.

- Practical Projects (P) The practical projects of this course weight 60% (12/20) of the final mark.

- (P1) Practical Project 1: Supervised Learning (Linear + Logistic Regression) (5/20).

- Due Date: Friday, October 9^th, 2026, 23:59:59.

- (P2) Practical Project 2: Dimensionality Reduction (5/20).

- Due Date: Friday, October 30^th, 2026, 23:59:59.

- (P3) Practical Project 3: Unsupervised Learning (5/20).

- Due Date: Friday, November 20^th, 2026, 23:59:59.

- (P4) Practical Project 4: Physics-Inspired Machine Learning (5/20).

- Due Date: Friday, December 11^th, 2026, 23:59:59.

- Written Test (F) Thursday, December 17^th, 2026, 18:00, Room 6.01.

- Mark (M) = [A >= 0.8] * (P * 12/20 + F * 8/20).

- This is a non-exam course Evaluation will be done mainly in a continuous way.