Statistical learning theory 2020
Teachers: Bruno Bauwens and Vladimir Podolskii
Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901
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
Email homeworks to Brbauwens <at> gmail.com. Start the subject line with SLT-HW. You may submit handwritten solutions.
Saturday 12 Dec and Tuesday 15 Dec, online. Choose your timeslot
Rules and questions. Version 30/11.
|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 26.11, HW 9.5||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|
|5 Dec||Recap of requested topics, 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||14h-18h||16h15-20h||Room S834 Pokrovkaya 11|
It is always good to send an email in advance. Questions are welcome.