Statistical learning theory 2018 2019

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General Information

The syllabus

Questions colloquium on 29 October. (Lectures 1-5, lectures 6-8 follow later.)

Deadline homework 1: October 2nd. Questions: see seminars 3 and 4.

Deadline homework 2: October 27nd. Questions: see seminars 5-8 below.

Deadline homework 3: TBA.


Intermediate exams: October 29th.

Course materials

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 .

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 16:45–19:00 15:05–18:00 Room 620

Russian texts

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 you can find brief and clear definitions.