Statistical learning theory 2020 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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(не показано 36 промежуточных версии этого же участника) | |||
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== General Information == | == General Information == | ||
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+ | [https://www.dropbox.com/s/a8dd33ousa76qad/grading.pdf?dl=0 Grading] | ||
Teachers: Bruno Bauwens and Vladimir Podolskii | Teachers: Bruno Bauwens and Vladimir Podolskii | ||
Строка 10: | Строка 12: | ||
- Group 2: Tuesday 18h, Nikita Lukyanenko, see [https://ruz.hse.ru ruz.hse.ru] | - Group 2: Tuesday 18h, Nikita Lukyanenko, see [https://ruz.hse.ru ruz.hse.ru] | ||
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− | + | == Reexam == | |
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− | + | 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. | |
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+ | 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). | ||
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+ | Zoom link 30 Jan: <span style="color:red">[https://zoom.us/j/99399187196]</span> | ||
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+ | == Colloquium == | ||
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+ | Saturday 12 Dec and Tuesday 15 Dec, online. Choose your [https://docs.google.com/spreadsheets/d/1tuBV6H_NdwRiR1YJdmv2vZLY0aFpM-Xh9DqZtYdTxm8/edit#gid=0 timeslot] | ||
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+ | [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&A] | ||
== Course materials == | == Course materials == | ||
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|| [https://www.dropbox.com/s/tot9akaoonja1zp/06slides.pdf?dl=0 slides6.pdf] | || [https://www.dropbox.com/s/tot9akaoonja1zp/06slides.pdf?dl=0 slides6.pdf] | ||
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− | || [https://www.dropbox.com/s/y7w3srgsrp9d7m0/06sem.pdf?dl=0 Problem list 6] <span style="color:red">Update | + | || [https://www.dropbox.com/s/y7w3srgsrp9d7m0/06sem.pdf?dl=0 Problem list 6] <span style="color:red">Update 10.11</span> |
− | || [https://www.dropbox.com/s/qc0847q8q8llgg2/06sol.pdf?dl=0 Solutions 6] | + | || [https://www.dropbox.com/s/qc0847q8q8llgg2/06sol.pdf?dl=0 Solutions 6] |
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| 31 Oct || Kernels | | 31 Oct || Kernels | ||
Строка 76: | Строка 82: | ||
|| [https://www.dropbox.com/s/yrptkeaydam7r2v/07slides.pdf?dl=0 slides7.pdf] | || [https://www.dropbox.com/s/yrptkeaydam7r2v/07slides.pdf?dl=0 slides7.pdf] | ||
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− | || [https://www.dropbox.com/s/81edvzrgiel3do6/07sem.pdf?dl=0 Problem list 7] | + | || [https://www.dropbox.com/s/81edvzrgiel3do6/07sem.pdf?dl=0 Problem list 7] <span style="color:red">Update 11.11, prob 7.6</span> |
|| [https://www.dropbox.com/s/xaoxh2i12x15jz6/07sol.pdf?dl=0 Solutions 7] | || [https://www.dropbox.com/s/xaoxh2i12x15jz6/07sol.pdf?dl=0 Solutions 7] | ||
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| 07 Nov || Adaboost | | 07 Nov || Adaboost | ||
− | || Chapt | + | || Chapt 6, Mohri et al |
+ | || [https://www.dropbox.com/s/2ied3qr0xrsb127/08slides.pdf?dl=0 slides8.pdf] | ||
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− | || | + | || [https://www.dropbox.com/s/i9jo9dlj06t51um/08sem.pdf?dl=0 Problem list 8] |
− | || | + | || [https://www.dropbox.com/s/1bxxzvorzbxpgji/08sol.pdf?dl=0 Solutions 8] |
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− | | 14 Nov || Online learning 1 | + | | 14 Nov || Online learning 1, Littlestone dimension, weighted majority algorithm |
+ | || Chapt 7, Mohri et al, and [http://machinelearning.ru/wiki/images/9/99/SLT%2C_lecture_85.pdf Животовский] | ||
+ | || [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf] | ||
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− | || | + | || [https://www.dropbox.com/s/k0ynyl5x874e0gq/09sem.pdf?dl=0 Problem list 9] <span style="color:red">Update 08.12, 9.4</span> |
− | || | + | || [https://www.dropbox.com/s/k2zpqnoiwe19osu/09sol.pdf?dl=0 Solutions 9] |
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− | | 21 Nov || Online learning | + | | 21 Nov || Online learning 2, Exponential weighted average algorithm, preceptron |
+ | || Chapt 7, Mohri et al | ||
+ | || [https://www.dropbox.com/s/rtlsy6ssm2yj2p0/09slides.pdf?dl=0 slides9.pdf] | ||
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− | || | + | || [https://www.dropbox.com/s/jh7krrihpc5f3ua/10sem.pdf?dl=0 Problem list 10] |
− | || | + | || [https://www.dropbox.com/s/tf8mdjxfbz86lj4/10sol.pdf?dl=0 Solutions 10] |
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− | | 28 Nov || | + | | 28 Nov || Online learning 3, perception, Winnow and online to batch conversion |
+ | || Chapt 7, Mohri et al | ||
+ | || [https://www.dropbox.com/s/ntkmnxhsvk9j38y/11slides.pdf?dl=0 slides11.pdf] | ||
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− | || | + | || [https://www.dropbox.com/s/py43d5k4mr7rv26/11sem.pdf?dl=0 Problem list 11] |
− | || | + | || [https://www.dropbox.com/s/fuj1wclaq7wwa7c/11sol.pdf?dl=0 Solutions 11] |
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− | | 5 Dec || | + | | 5 Dec || Recap of requested topics, Q&A |
− | || | + | || [https://www.dropbox.com/s/ugiqfsk2mg01262/QandA.pdf?dl=0 Q&A] |
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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 == | ||
Строка 137: | Строка 137: | ||
! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ||
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− | | Bruno Bauwens || 14h-18h || 16h15-20h || || || || Room S834 Pokrovkaya 11 | + | | [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] (email in advance) || 14h-18h || 16h15-20h || || || || Room S834 Pokrovkaya 11 |
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Текущая версия на 10:02, 3 сентября 2021
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
Reexam
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: [1]
Colloquium
Saturday 12 Dec and Tuesday 15 Dec, online. Choose your timeslot
Rules and questions. version 06/12. Q&A
Course materials
Date | Summary | Lecture notes | Slides | Video | Problem list | Solutions |
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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
Office hours
Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
---|---|---|---|---|---|---|
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.