Statistical learning theory

General Information

Syllabus for the 1st module

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, agnostic PAC-learning, structural risk minimization
26 sept	Computational learning theory
3 okt	Boosting: the adaBoost algorithm
10 okt	Boosting: several other algorithms
17 okt	Online learning algorithms

The materials of the first 3 lectures are covered in chapters 1-6 of the following book: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.

This book gives a gentle introduction and repeats all necessary background from probability theory and statistics.

Office hours

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