Statistical learning theory — различия между версиями

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The materials of the first 3 lectures are covered in chapters 1-6 of the following book:
 
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.
 
Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.
  
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This book gives a gentle introduction and repeats all necessary background from probability theory and statistics.
 
This book gives a gentle introduction and repeats all necessary background from probability theory and statistics.

Версия 19:06, 13 сентября 2017

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