Statistical learning theory
Will be placed here on Sat 30/09. The deadline for submission is Thursday 12th of Okt.
Will be placed here. The deadline for submission is Saturday 21th of Okt.
|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.
||Bruno Bauwens||15:05–18:00||15:05–18:00||Room 620|