# Statistical learning theory 2020

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## General Information

Teachers: Bruno Bauwens and Vladimir Podolskii

Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901

Seminars
- Group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom https://zoom.us/j/94186131884,
- Group 2: Tuesday 18h, Nikita Lukyanenko, see ruz.hse.ru

Practical information on telegram group

Homeworks: deadlines every 2 weeks, before the lecture.
- Sat 3 Oct: see problem lists 1 and 2
- Sat 17 Oct: see problem lists 3 and 4
- Sat 31 Oct: see problem list 5
- Sat 14 Nov: see problem lists 6 and 7 Update 11.11
- Sat 28 Nov: see problem lists 8 and 9
- Sat 12 Dec: see problem lists 10 and 11

Email homeworks to Brbauwens <at> gmail.com. Start the subject line with SLT-HW. You may submit handwritten solutions.

## Colloquium

Saturday 12 Dec and Tuesday 15 Dec, online

What: basic definitions, proofs of 2-3 main results in each lecture. (A list with questions will be given in a few days.)

How: you receive a question, you may look up the answer for a short time (say 2 min) and you must explain the proof. Basic questions are asked to test understanding.

## Course materials

Date Summary Lecture notes Slides Video Problem list Solutions
12 Sept Introduction and sample complexity in the realizable setting lecture1.pdf slides1.pdf Problem list 1 Update 26.09, prob 1.7 Solutions 1
19 Sept VC-dimension and sample complexity lecture2.pdf slides2.pdf Chapt 2,3 Problem list 2 Solutions 2
26 Sept Risk bounds and the fundamental theorem of statistical learning theory lecture3.pdf slides3.pdf Problem list 3 Solutions 3
03 Oct Rademacher complexity lecture4.pdf slides4.pdf Problem list 4 Update 23.10, prob 4.1d Solutions 4
10 Oct Support vector machines and risk bounds Chapt 5, Mohri et al, see below slides5.pdf Problem list 5 Update 29.10, typo 5.8 Solutions 5
17 Oct Support vector machines and recap Chapt 5, Mohri et al. slides6.pdf Problem list 6 Update 10.11 Solutions 6
31 Oct Kernels lecture7.pdf slides7.pdf Problem list 7 Update 11.11, prob 7.6 Solutions 7
07 Nov Adaboost Chapt 6, Mohri et al slides8.pdf Problem list 8 Solutions 8
14 Nov Online learning 1 Chapt 7, Mohri et al [1] Problem list 9
21 Nov Online learning 2
28 Nov Unsupervised learning
5 Dec Recap of requested topics, Q&A

The lectures in October are based on the book: Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. This book can be downloaded from http://gen.lib.rus.ec/ .

In November, we follow the lecture notes by Н. К. Животовский

## Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 14h-18h 16h15-20h Room S834 Pokrovkaya 11

It is always good to send an email in advance. Questions are welcome.