2023/2024
***Instructors:Date  Time  Room number  Instructor  Session  Topics  Material 
Tuesday October 3 
08:3010:30  1Z34  N. Vayatis  Lecture #1  Chapter 1  Optimality in binary
classification Data/Objectives/Optimal elements/ERM 
Slides LDA 
Tuesday October 10 
08:3010:30  1Z34  N. Vayatis  Lecture #2  Chapter 1  Optimality in statistical learning Other problems/Complexity of learning 
Slides 
Tuesday October 17 
08:3010:30  1Z34  G. Serré  Exercise session #1  Optimal elements 
Set 
Tuesday October 24 
08:3010:30  1Z34  N. Vayatis  Lecture #3  Chapter 2  Mathematical foundations (I) Probabilistic inequalities, complexity measures 
Slides 
Tuesday October 31 
08:3010:30  1Z34  M. Garin  Exercise session #2  Inequalities, Rademacher complexity, VC
dimension 
Set 
Tuesday November 7 
08:3010:30  1Z34  Partial exam  Mandatory No documents 


Tuesday November 14 
08:3010:30  1Z34  N. Vayatis  Lecture #4  Chapter 2  Mathematical foundations (II) Regularization and stability 
Slides Link 
Tuesday November 21 
08:3010:30  1Z34  N. Vayatis  Lecture #5  Chapter 3  Consistency of Machine Learning methods (I) Margin bounds and application to SVM 
Slides 
Tuesday November 28  08:3010:30  1Z34  G. Serré  Exercise session #3  Consistency and convergence bounds 
Set 
Tuesday December 5 
08:3010:30  1Z34  N. Vayatis  Lecture #6  Chapter 3  Consistency of Machine Learning methods (II) Ensemble methods: Bagging, Random Forests, Boosting 
Slides 
Tuesday December 12  08:3010:30  1Z34  N. Vayatis  Lecture #7  Chapter
3  Consistency of Machine Learning methods (III) Neural networks, Mirror Descent 
Slides 
Tuesday December 19  08:3010:30  1Z34  M. Garin  Exercise session #4  Course wrapup 
Set 
Monday January 8 
08:3010:30  1Z34  Final exam  Mandatory Documents allowed 