2022/2023
***Instructors:Date  Time  Room number  Instructor  Session  Topics  Material 
Monday October 3 
11:3013:30  1Z34  N. Vayatis  Lecture #1  Chapter 1  Optimality in binary
classification Data/Objectives/Optimal elements/ERM 
Slides 
Tuesday October 11 
08:0010:00  1Z34  N. Vayatis  Lecture #2  Chapter 1  Optimality in statistical learning Other problems/Complexity of learning 
Slides 
Monday October 17 
09:3011:30  1Z56  M. Garin  Exercise session #1  Optimal elements 
Set 
Monday October 24 
12:0014:00  1Z34  N. Vayatis  Lecture #3  Chapter 2  Mathematical foundations (I) Probabilistic inequalities, complexity measures 
Slides 
Friday November 4 
10:0012:00  1Z34  M. Garin  Exercise session #2  Inequalities, Rademacher complexity, VC
dimension 
Set 
Tuesday November 8 
09:0011:00  1Z34  Partial exam  Mandatory No documents 


Monday November 21 
14:0016:00  1Z34  N. Vayatis  Lecture #4  Chapter 2  Mathematical foundations (II) Regularization and stability 
Slides Link 
Tuesday November 22 
09:0011:00  1Z28  N. Vayatis  Lecture #5  Chapter 3  Consistency of Machine Learning methods (I) Margin bounds and application to SVM 
Slides 
Monday November 28  16:0018:00  1Z34  M. Garin  Exercise session #3  Consistency and convergence bounds 
Set 
Monday December 5 
14:0016:00  1Z34  N. Vayatis  Lecture #6  Chapter 3  Consistency of Machine Learning methods (II) Neural networks, Bagging, Random Forests 
Slides 
Monday December 12  14:0016:00  1Z34  M. Garin  Exercise session #4  Course wrapup 
Set 
Monday January 9 
10:0012:00  1Z34  Final exam  Mandatory Documents allowed 