Statistical learning theory

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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 3th lecture Only the first part of the notes.
26 sept 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