Statistical learning theory 2021: различия между версиями
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Bbauwens (обсуждение | вклад) Нет описания правки |
Bbauwens (обсуждение | вклад) Нет описания правки |
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| 7 Sept | | 7 Sept | ||
|| Introduction, the online mistake bound model, weighted majority and perceptron algorithms | || Introduction, the online mistake bound model, the weighted majority and perceptron algorithms | ||
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|| | | 14 Sept | ||
|| The standard optimal algorithm, prediction with expert advice, exponentially weighted algorithm | |||
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| 21 Sept | |||
|| Better mistake bounds using VC-dimensions. Recap probability theory. Leave on out risk for SVM. | |||
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|| Part 2. Supervised classification || || || | |||
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| 28 Sept | |||
|| Sample complexity in the realizable setting, simple example and bounds using VC-dimension | |||
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| 5 Oct | |||
|| Risk decomposition and the fundamental theorem of statistical learning theory | |||
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| 12 Oct | |||
|| Rademacher complexity | |||
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| 26 Oct | |||
|| Support vector machines and margin risk bounds | |||
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<!-- | 12 Sept || Introduction and | || | ||
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| 2 Nov | |||
|| AdaBoost and risk bounds | |||
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|| Part 3. Other topics || || || | |||
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| 9 Nov | |||
|| Clustering | |||
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| 16 Nov | |||
|| Dimensionality reduction and the Johnson-Lindenstrauss lemma | |||
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<!-- | 12 Sept || Introduction and | |||
|| [https://www.dropbox.com/s/kicoo9xf356eam5/01lect.pdf?dl=0 lecture1.pdf] | || [https://www.dropbox.com/s/kicoo9xf356eam5/01lect.pdf?dl=0 lecture1.pdf] | ||
|| [https://www.dropbox.com/s/pehka8xyu5hlpis/slides01.pdf?dl=0 slides1.pdf] | || [https://www.dropbox.com/s/pehka8xyu5hlpis/slides01.pdf?dl=0 slides1.pdf] | ||
Версия от 09:10, 2 сентября 2021
General Information
Teachers: Bruno Bauwens and Nikita Lukianenko
Lectures: Tuesdays 9h30 - 10h50, zoom
Seminars: Tuesday 11h10 - 12h30
Practical information on telegram group
Course materials
| Date | Summary | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|
| Part 1. Online learning | ||||
| 7 Sept | Introduction, the online mistake bound model, the weighted majority and perceptron algorithms | |||
| 14 Sept | The standard optimal algorithm, prediction with expert advice, exponentially weighted algorithm | |||
| 21 Sept | Better mistake bounds using VC-dimensions. Recap probability theory. Leave on out risk for SVM. | |||
| Part 2. Supervised classification | ||||
| 28 Sept | Sample complexity in the realizable setting, simple example and bounds using VC-dimension | |||
| 5 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | |||
| 12 Oct | Rademacher complexity | |||
| 26 Oct | Support vector machines and margin risk bounds | |||
| 2 Nov | AdaBoost and risk bounds | |||
| Part 3. Other topics | ||||
| 9 Nov | Clustering | |||
| 16 Nov | Dimensionality reduction and the Johnson-Lindenstrauss lemma |
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.