Statistical learning theory

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General Information

Syllabus for the 1st module


Homework module 1 The deadline for submission is Thursday 12th of Oktober. Submit either by email or in paper during the lecture or place it under the door of office 620.

Defenses of the homework will happen from 13th of Okt. till 30 Okt.


Will be placed here. The deadline for submission is Saturday 21th of Okt.

Course materials

Date Summary Lecture notes Problem list
5 sept PAC-learning and VC-dimension: definitions 1st and 2nd lecture Updated on 13th of Sept. Problem list 1
12 sept PAC-learning and VC-dimension: proof of fundamental theorem Problem list 2
19 sept Sauer's lemma, neural networks and agnostic PAC-learning 3th lecture Updated on the 23th of Sept. Problem list 3
26 sept Agnostic PAC-learning and Computational learning theory
3 okt Boosting: the adaBoost algorithm
10 okt Boosting: several other algorithms
17 okt Online learning algorithms

A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 15:05–18:00 15:05–18:00 Room 620
Quentin Paris