2026/2027
***Instructors:| Date | Time | Room number | Instructor | Session | Topics | Material |
| Tuesday September 29 |
10:00-12:00 | 1Z56 | Nicolas | Lecture #1 | Chapter 1 - Optimality in binary
classification Data/Objectives/Optimal elements/ERM |
Slides |
| Tuesday October 6 |
10:00-12:00 | 1B10 | Nicolas | Lecture #2 | Chapter 2- - Mathematical foundations (I) Probabilistic inequalities, complexity measures |
Slides |
| Tuesday October 13 |
10:00-12:00 | 1Z25 | Perceval | Exercise session #1 | Optimal elements |
E-Set |
| Tuesday October 20 |
10:00-12:00 | 1Z56 | Nicolas | Lecture #3 | Chapter 2 - Mathematical foundations (II) Regularization and stability |
Slides |
| Tuesday October 27 |
10:00-12:00 | 1Z25 | Perceval | Exercise session #2 | Inequalities, Rademacher complexity, VC
dimension |
E-Set |
| Tuesday November 3 |
10:00-12:00 | 1Z61 | Partial exam - Mandatory No documents, no electronic devices |
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| Tuesday November 10 |
10:00-12:00 | 1Z56 | Nicolas | Lecture #4 | Chapter 3 - Consistency of Machine Learning methods (I) Margin bounds and application to SVM |
Slides |
| Tuesday November 17 |
10:00-12:00 | 1B10 | Nicolas | Lecture #5 |
Chapter 3 - Consistency of Machine Learning methods (II) Ensemble methods: Bagging, Random Forests, Boosting |
Slides |
| Tuesday November 24 | 10:00-12:00 | 1Z25 | Perceval | Exercise session #3 | Consistency and convergence bounds | E-Set |
| Tuesday December 1 |
10:00-12:00 | 1Z25 | Nicolas | Lecture #6 | Chapter 3 - Consistency of Machine Learning methods (III) Neural networks, Mirror descent |
Slides |
| Tuesday December 8 |
10:00-12:00 | 1B10 | Perceval | Exercise session #4 | Wrap-up | E-Set |
| Tuesday December 15 |
10:00-12:00 | 1Z56 | Final exam - Mandatory No documents, no electronic devices |