Statistical learning theory 2020
Teachers: Bruno Bauwens and Vladimir Podolskii
Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901
Date: Sat 23 Jan and 30 Jan 14h
Consists of a retake of the colloquium and the problems exam. Somewhere in the first hour (depending on the availability of the teacher), you redo the colloquium and in the remaining time, you solve 4 or 5 problems similar as in the exam on Dec 23rd.
In the calculation of the grades, the homework results are dropped, and the final grade consists of the average of the colloquium part and the problems part (with equal weight).
Zoom link 30 Jan: 
Saturday 12 Dec and Tuesday 15 Dec, online. Choose your timeslot
|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, Littlestone dimension, weighted majority algorithm||Chapt 7, Mohri et al, and Животовский||slides9.pdf||Problem list 9 Update 08.12, 9.4||Solutions 9|
|21 Nov||Online learning 2, Exponential weighted average algorithm, preceptron||Chapt 7, Mohri et al||slides9.pdf||Problem list 10||Solutions 10|
|28 Nov||Online learning 3, perception, Winnow and online to batch conversion||Chapt 7, Mohri et al||slides11.pdf||Problem list 11||Solutions 11|
|5 Dec||Recap of requested topics, Q&A||Q&A|
The lectures in October and November 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/ .
For online learning, we also study a few topics from lecture notes by Н. К. Животовский
|Bruno Bauwens, Zoom (email in advance)||14h-18h||16h15-20h||Room S834 Pokrovkaya 11|
It is always good to send an email in advance. Questions are welcome.