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	<title>Statistical learning theory 2020 - История изменений</title>
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	<updated>2026-06-06T13:17:26Z</updated>
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		<id>https://wikicshse.ru/index.php?title=Statistical_learning_theory_2020&amp;diff=712&amp;oldid=prev</id>
		<title>imported&gt;Bbauwens: Migrated current public revision from wiki.cs.hse.ru</title>
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		<updated>2021-09-03T07:02:25Z</updated>

		<summary type="html">&lt;p&gt;Migrated current public revision from wiki.cs.hse.ru&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Новая страница&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== General Information ==&lt;br /&gt;
&lt;br /&gt;
[https://www.dropbox.com/s/a8dd33ousa76qad/grading.pdf?dl=0 Grading]&lt;br /&gt;
&lt;br /&gt;
Teachers: Bruno Bauwens and Vladimir Podolskii&lt;br /&gt;
&lt;br /&gt;
Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901&lt;br /&gt;
&lt;br /&gt;
Seminars &amp;lt;br&amp;gt;&lt;br /&gt;
- Group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom https://zoom.us/j/94186131884, &amp;lt;br&amp;gt;&lt;br /&gt;
- Group 2: Tuesday 18h, Nikita Lukyanenko, see [https://ruz.hse.ru ruz.hse.ru]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Reexam ==&lt;br /&gt;
&lt;br /&gt;
Date: Sat 23 Jan and 30 Jan 14h&lt;br /&gt;
&lt;br /&gt;
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.&lt;br /&gt;
&lt;br /&gt;
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).&lt;br /&gt;
&lt;br /&gt;
Zoom link 30 Jan: &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;[https://zoom.us/j/99399187196]&amp;lt;/span&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Colloquium ==&lt;br /&gt;
&lt;br /&gt;
Saturday 12 Dec and Tuesday 15 Dec, online. Choose your [https://docs.google.com/spreadsheets/d/1tuBV6H_NdwRiR1YJdmv2vZLY0aFpM-Xh9DqZtYdTxm8/edit#gid=0 timeslot] &lt;br /&gt;
&lt;br /&gt;
[https://www.dropbox.com/s/0f4v5vp0fsz7e34/colloqQuest.pdf?dl=0 Rules and questions.] version 06/12. [https://www.dropbox.com/s/ugiqfsk2mg01262/QandA.pdf?dl=0 Q&amp;amp;A]&lt;br /&gt;
&lt;br /&gt;
== Course materials ==&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
! Date !! Summary !! Lecture notes !! Slides !! Video !! Problem list !! Solutions&lt;br /&gt;
|-&lt;br /&gt;
| 12 Sept || Introduction and sample complexity in the realizable setting &lt;br /&gt;
|| [https://www.dropbox.com/s/kicoo9xf356eam5/01lect.pdf?dl=0 lecture1.pdf] &lt;br /&gt;
|| [https://www.dropbox.com/s/pehka8xyu5hlpis/slides01.pdf?dl=0 slides1.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/fbdew1vdzskenie/01sem.pdf?dl=0 Problem list 1] &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 26.09, prob 1.7&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/rn8nv9y0db61a0h/01sol.pdf?dl=0 Solutions 1]&lt;br /&gt;
|-&lt;br /&gt;
| 19 Sept || VC-dimension and sample complexity &lt;br /&gt;
|| [https://www.dropbox.com/s/ayry6kp91h5s1nv/02lect.pdf?dl=0 lecture2.pdf] &lt;br /&gt;
|| [https://www.dropbox.com/s/6p6h1ooy4i5wt1t/02slides.pdf?dl=0 slides2.pdf]&lt;br /&gt;
|| [https://youtu.be/SBoffzKZebg Chapt 2,3]&lt;br /&gt;
|| [https://www.dropbox.com/s/4qn4qzr6mgu9lt3/02sem.pdf?dl=0 Problem list 2]&lt;br /&gt;
|| [https://www.dropbox.com/s/0g5gw3yrjjjzz07/02sol.pdf?dl=0 Solutions 2]&lt;br /&gt;
|-&lt;br /&gt;
| 26 Sept || Risk bounds and the fundamental theorem of statistical learning theory &lt;br /&gt;
|| [https://www.dropbox.com/s/njekia6g8t0x5mb/03lect.pdf?dl=0 lecture3.pdf]&lt;br /&gt;
|| [https://www.dropbox.com/s/at4eph4mv9gfnp1/03slides.pdf?dl=0 slides3.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/nvb25e0ccebbz2a/03sem.pdf?dl=0 Problem list 3]&lt;br /&gt;
|| [https://www.dropbox.com/s/5jbl0xul25mrbg1/03sol.pdf?dl=0 Solutions 3]&lt;br /&gt;
|-&lt;br /&gt;
| 03 Oct || Rademacher complexity &lt;br /&gt;
|| [https://www.dropbox.com/s/ggw79gau85a4mcl/04lect.pdf?dl=0 lecture4.pdf] &lt;br /&gt;
|| [https://www.dropbox.com/s/pd2ockzxqdfo66t/04slides.pdf?dl=0 slides4.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/rbx6jwlusnwhkzn/04sem.pdf?dl=0 Problem list 4] &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 23.10, prob 4.1d&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/nhxkxfjajzsgfnf/04sol.pdf?dl=0 Solutions 4]&lt;br /&gt;
|-&lt;br /&gt;
| 10 Oct || Support vector machines and risk bounds&lt;br /&gt;
|| Chapt 5, Mohri et al, see below &lt;br /&gt;
|| [https://www.dropbox.com/s/q2onm9o6wgceg5i/05slides.pdf?dl=0 slides5.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/upv70of97fqpx5f/05sem.pdf?dl=0 Problem list 5] &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 29.10, typo 5.8&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/jfneptto1qoug1g/05sol.pdf?dl=0 Solutions 5]&lt;br /&gt;
|-&lt;br /&gt;
| 17 Oct || Support vector machines and recap&lt;br /&gt;
|| Chapt 5, Mohri et al.&lt;br /&gt;
|| [https://www.dropbox.com/s/tot9akaoonja1zp/06slides.pdf?dl=0 slides6.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/y7w3srgsrp9d7m0/06sem.pdf?dl=0 Problem list 6]  &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 10.11&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/qc0847q8q8llgg2/06sol.pdf?dl=0 Solutions 6]  &lt;br /&gt;
|-&lt;br /&gt;
| 31 Oct || Kernels&lt;br /&gt;
|| [https://www.dropbox.com/s/lzhbe7sb4aw49d4/07lec.pdf?dl=0 lecture7.pdf]&lt;br /&gt;
|| [https://www.dropbox.com/s/yrptkeaydam7r2v/07slides.pdf?dl=0 slides7.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/81edvzrgiel3do6/07sem.pdf?dl=0 Problem list 7] &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 11.11, prob 7.6&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/xaoxh2i12x15jz6/07sol.pdf?dl=0 Solutions 7]&lt;br /&gt;
|-&lt;br /&gt;
| 07 Nov || Adaboost &lt;br /&gt;
|| Chapt 6, Mohri et al&lt;br /&gt;
|| [https://www.dropbox.com/s/2ied3qr0xrsb127/08slides.pdf?dl=0 slides8.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/i9jo9dlj06t51um/08sem.pdf?dl=0 Problem list 8]&lt;br /&gt;
|| [https://www.dropbox.com/s/1bxxzvorzbxpgji/08sol.pdf?dl=0 Solutions 8]&lt;br /&gt;
|-&lt;br /&gt;
| 14 Nov || Online learning 1, Littlestone dimension, weighted majority algorithm&lt;br /&gt;
|| Chapt 7, Mohri et al, and [http://machinelearning.ru/wiki/images/9/99/SLT%2C_lecture_85.pdf Животовский]&lt;br /&gt;
|| [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/k0ynyl5x874e0gq/09sem.pdf?dl=0 Problem list 9] &amp;lt;span style=&amp;quot;color:red&amp;quot;&amp;gt;Update 08.12, 9.4&amp;lt;/span&amp;gt;&lt;br /&gt;
|| [https://www.dropbox.com/s/k2zpqnoiwe19osu/09sol.pdf?dl=0 Solutions 9]&lt;br /&gt;
|-&lt;br /&gt;
| 21 Nov || Online learning 2, Exponential weighted average algorithm, preceptron&lt;br /&gt;
|| Chapt 7, Mohri et al&lt;br /&gt;
|| [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/jh7krrihpc5f3ua/10sem.pdf?dl=0 Problem list 10]&lt;br /&gt;
|| [https://www.dropbox.com/s/tf8mdjxfbz86lj4/10sol.pdf?dl=0 Solutions 10]&lt;br /&gt;
|-&lt;br /&gt;
| 28 Nov || Online learning 3, perception, Winnow and online to batch conversion&lt;br /&gt;
|| Chapt 7, Mohri et al&lt;br /&gt;
|| [https://www.dropbox.com/s/ntkmnxhsvk9j38y/11slides.pdf?dl=0 slides11.pdf]&lt;br /&gt;
|| &lt;br /&gt;
|| [https://www.dropbox.com/s/py43d5k4mr7rv26/11sem.pdf?dl=0 Problem list 11]&lt;br /&gt;
|| [https://www.dropbox.com/s/fuj1wclaq7wwa7c/11sol.pdf?dl=0 Solutions 11]&lt;br /&gt;
|-&lt;br /&gt;
| 5 Dec || Recap of requested topics, Q&amp;amp;A&lt;br /&gt;
|| [https://www.dropbox.com/s/ugiqfsk2mg01262/QandA.pdf?dl=0 Q&amp;amp;A]&lt;br /&gt;
|| &lt;br /&gt;
|| &lt;br /&gt;
|| &lt;br /&gt;
||&lt;br /&gt;
|-&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!-- A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book:&lt;br /&gt;
Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.--&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The lectures in October and November are based on the book:&lt;br /&gt;
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/ .&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== Office hours ==&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! &lt;br /&gt;
|-&lt;br /&gt;
|  [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] (email in advance) || 14h-18h  || 16h15-20h || || ||  || Room&amp;amp;nbsp;S834 Pokrovkaya 11&lt;br /&gt;
|-&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
It is always good to send an email in advance. Questions are welcome.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--&lt;br /&gt;
== Russian texts  ==&lt;br /&gt;
&lt;br /&gt;
The following links might help students who have trouble with English.  A [http://www.machinelearning.ru/wiki/images/d/d9/Voron-2011-tnop.pdf  lecture] on VC-dimensions was given by K. Vorontsov.&lt;br /&gt;
A [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) course] on Statistical Learning Theory by Nikita Zhivotovsky is given at MIPT. Some short description about PAC learning on p136 in the [http://gen.lib.rus.ec/search.php?req=%D0%9D%D0%B0%D1%83%D0%BA%D0%B0+%D0%B8+%D0%B8%D1%81%D0%BA%D1%83%D1%81%D1%81%D1%82%D0%B2%D0%BE+%D0%BF%D0%BE%D1%81%D1%82%D1%80%D0%BE%D0%B5%D0%BD%D0%B8%D1%8F+%D0%B0%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC%D0%BE%D0%B2%2C+%D0%BA%D0%BE%D1%82%D0%BE%D1%80%D1%8B%D0%B5+%D0%B8%D0%B7%D0%B2%D0%BB%D0%B5%D0%BA%D0%B0%D1%8E%D1%82+%D0%B7%D0%BD%D0%B0%D0%BD%D0%B8%D1%8F+%D0%B8%D0%B7+%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85&amp;amp;lg_topic=libgen&amp;amp;open=0&amp;amp;view=simple&amp;amp;res=25&amp;amp;phrase=0&amp;amp;column=def book] &lt;br /&gt;
``Наука и искусство построения алгоритмов, которые извлекают знания из данных&amp;#039;&amp;#039;, Петер Флах. On [http://www.machinelearning.ru machinelearning.ru] &lt;br /&gt;
you can find brief and clear definitions.&lt;br /&gt;
--&amp;gt;&lt;/div&gt;</summary>
		<author><name>imported&gt;Bbauwens</name></author>
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