Statistical learning theory 2021 — различия между версиями
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Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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|| Dimensionality reduction and the Johnson-Lindenstrauss lemma | || Dimensionality reduction and the Johnson-Lindenstrauss lemma | ||
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| + | | 30 Nov | ||
| + | || Active learning | ||
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| + | |- 7 or 14 Dec | ||
| + | || Colloquium | ||
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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/ . | ||
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== Office hours == | == Office hours == | ||
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! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ||
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| − | | [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] (email in advance) || | + | | [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] (email in advance) || || 12h30-14h30 || || || 14h-20h || Room S834 Pokrovkaya 11 |
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| − | It is always good to send an email in advance. Questions are welcome. | + | It is always good to send an email in advance. Questions and feedback are welcome. |
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Версия 12:37, 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 | Kernels: risk bounds, design, and representer theorem | |||
| 9 Nov | AdaBoost and risk bounds | |||
| Part 3. Other topics | ||||
| 16 Nov | Clustering | |||
| 23 Nov | Dimensionality reduction and the Johnson-Lindenstrauss lemma | |||
| 30 Nov | Active learning | |||
| Colloquium |
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/ .
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| Bruno Bauwens, Zoom (email in advance) | 12h30-14h30 | 14h-20h | Room S834 Pokrovkaya 11 |
It is always good to send an email in advance. Questions and feedback are welcome.
