# Statistical learning theory 2018 2019 — различия между версиями

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## General Information

The syllabus

Questions colloquium on 29 October. (Lectures 1-8 updated 24/10.)

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: December 11nd. Questions: see seminars 9-12 below.

Intermediate exams: October 29th.

Final exam: December 20th, same system as for intermediate exams. Theory questions

Consultation: December 17th, no lecture. Students can ask questions and ask for solutions of exercises.

## 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 Solutions 1
10 Sept VC-dimension and growth functions lecture2.pdf updated 23/09 Problem list 2 Solutions 2
17 Sept Proof that finite VC-dimension implies PAC-learnability lecture3.pdf updated 23/09 Problem list 3 Solutions 3
24 Sept Applications to decision trees and threshold neural networks. Agnostic PAC-learnability. lecture4.pdf Problem list 4 Solution 4
1 Oct Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization lecture5.pdf 14/10 Problem list 5 Solution 5
9 Oct Boosting, Mohri's book pages 121-131. lecture6.pdf 23/10 Problem list 6 No solution.
15 Oct Rademacher complexity and contraction lemma (=Talagrand's lemma), Mohri's book pages 33-41 and 78-79 lecture7.pdf Problem list 7 See lecture7.pdf
21 Oct Margin theory and risk bounds for boosting. lecture8.pdf Problem list 8 See lecture6.pdf for ex. 8.6.
12 Nov Deep boosting, we study the paper Multi-class deep boosting, V. Kuznetsov, M Mohri, and U. Syed, Advances in Neural Information Processing Systems, p2501--2509, 2014. Notes will be provided. Problem list 9 Solutions 9.
19 Nov Support vector machines, primal and dual optimization problem, risk bounds. See chapt. 5 of Mohri's book Problem list 10
26 Nov Kernels, Kernel reproducing Hilbert spaces, representer theorem, examples of kernels lecture11.pdf Problem set 11 Solutions: see lecture11.pdf
3 Dec A polynomial time improper learning algorithm for constant depth L1-regularized neural networks, from this paper. Online algorithms: halving algorithm, weighted and exponentially weighted average algorithms. See Mohri's book Sections 7.1 and 7.2. lecture12.pdf Problem list 12
10 Dec We finish online learning. Discuss the algorithm from this paper.

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/ .

## 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 machinelearning.ru you can find brief and clear definitions.