Statistical learning theory

General Information

Syllabus for the 1st module

Homework

Will be placed here on Sat 30/09. The deadline for submission is Thursday 12th of Okt.

Project

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
1	Bruno Bauwens		15:05–18:00			15:05–18:00	Room 620
2	Quentin Paris