Statistical learning theory 2020

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General Information

Teachers: Bruno Bauwens and Vladimir Podolskii

Lectures: Saturday 9h30 - 10h50, zoom

- Group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom,
- Group 2: Tuesday 18h, Nikita Lukyanenko, see

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
- Sat 28 Nov: see problem lists 8 and 9
- Sat 12 Dec: see problem lists 10 and 11


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

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 09.10, prob 4.3,4.6 Solutions 4
10 Oct Support vector machines and risk bounds Chapt 5, Mohri et al, see below slides5.pdf Problem list 5 Update 12.10, added background info Solutions 5
17 Oct Support vector machines and kernels. lecture6.pdf slides6.pdf Problem list 6
31 Oct Adaboost
07 Nov Online learning 1
14 Nov Online learning 2
21 Nov Active learning
28 Nov Unsupervised learning
5 Dec Optional: Neural networks and stochastic gradient descent
12 Dec Optional: Neural networks and stochastic gradient descent

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 .

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.