Statistical learning theory 2018 2019
Questions colloquium on 29 October. (Lectures 1-8 updated 24/10.)
Deadline homework 2: October 27nd. Questions: see seminars 5-8 below.
Deadline homework 3: TBA.
Intermediate exams: October 29th.
|Date||Summary||Lecture notes||Problem list||Solutions|
|3 Sept||PAC-learning in the realizable setting definitions||lecture1.pdf updated 23/09||Problem list 1|
|10 Sept||VC-dimension and growth functions||lecture2.pdf updated 23/09||Problem list 2|
|17 Sept||Proof that finite VC-dimension implies PAC-learnability||lecture3.pdf updated 23/09||Problem list 3|
|24 Sept||Applications to decision trees and threshold neural networks. Agnostic PAC-learnability.||lecture4.pdf||Problem list 4|
|1 Oct||Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization||lecture5.pdf 14/10||Problem list 5|
|9 Oct||Boosting, Mohri's book pages 121-131.||lecture6.pdf 23/10||Problem list 6|
|15 Oct||Rademacher complexity and contraction lemma (=Talagrand's lemma), Mohri's book pages 33-41 and 78-79||lecture7.pdf||Problem list 7|
|21 Oct||Margin theory and risk bounds for boosting.||lecture8.pdf||Problem list 8|
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.
Afterward, we hope to cover chapters 1-8 from the book: Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012. These books can be downloaded from http://gen.lib.rus.ec/ .
|Bruno Bauwens||16:45–19:00||15:05–18:00||Room 620|
The following links might help students who have trouble with English. A lecture on VC-dimensions was given by K. Vorontsov. A course on Statistical Learning Theory by Nikita Zhivotovsky is given at MIPT. Some short description about PAC learning on p136 in the book ``Наука и искусство построения алгоритмов, которые извлекают знания из данных, Петер Флах. On machinelearning.ru you can find brief and clear definitions.