Statistical learning theory 2020 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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| 14 Nov || Online learning 1 | | 14 Nov || Online learning 1 | ||
− | || Chapt 7, Mohri et al | + | || Chapt 7, Mohri et al, |
|| [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf] | || [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf] | ||
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| 21 Nov || Online learning 2 | | 21 Nov || Online learning 2 | ||
− | || | + | || Chapt 7, Mohri et al |
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Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.--> | Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.--> | ||
− | The lectures in October are based on the book: | + | 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/ . | 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 [http://machinelearning.ru/wiki/index.php?title=%D0%A2%D0%B5%D0%BE%D1%80%D0%B8%D1%8F_%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8F_(%D0%BA%D1%83%D1%80%D1%81_%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D0%B9%2C_%D0%9D._%D0%9A._%D0%96%D0%B8%D0%B2%D0%BE%D1%82%D0%BE%D0%B2%D1%81%D0%BA%D0%B8%D0%B9) lecture notes] by Н. К. Животовский | |
== Office hours == | == Office hours == |
Версия 18:33, 20 ноября 2020
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, | slides9.pdf | Problem list 9 | Solutions 9 | |
21 Nov | Online learning 2 | Chapt 7, Mohri et al | ||||
28 Nov | Unsupervised learning | |||||
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
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.