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;

- 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 practical classes.

- Practical Projects (P) The practical projects of this course weight 50% (10/20) of the final mark.

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

- Due Date: Thursday, October 3^rd, 2024, 23:59:59.

- (P2) Practical Project 2: Supervised Learning (Classification) (5/20).

- Due Date: Thursday, October 31^st, 2024, 23:59:59.

- (P3) Practical Project 3: Convolutional Neural Networks - CNNs (5/20).

- Due Date: Thursday, November 21^st, 2024, 23:59:59.

- (P4) Practical Project 4: Unsupervised Learning (5/20).

- Due Date: Thursday, December 19^th, 2024, 23:59:59.

- To get approved at the course, a minimal mark of 8/20 should be obtained in the practical project part.

- Written Test (F) Thursday, December 19^th, 2024, 14:00, Room 6.03.

- Mark (M) M = [A >= 0.8] * (P * 10/20 + F * 10/20).

- Admission to Exams Students with M >= 6 are admitted to final exams.

- The practical projects mark is considered in all examination epochs.