Statistical learning theory 2018 2019 — различия между версиями
Bbauwens (обсуждение | вклад) (Новая страница: « == Information == The syllabus == General Information == [https://www.dropbox.com/s/r5u7gl33berpokv/syllabusStatisticalLearning.pdf?dl=0 Syllabus for the 1st…») |
Bbauwens (обсуждение | вклад) |
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== Information == | == Information == | ||
| − | The syllabus | + | The [https://www.dropbox.com/s/8iivgt3a96yw308/syllabus_StatisticalLearning_Bach_2018_2019.pdf?dl=0 syllabus] |
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! Date !! Summary !! Lecture notes !! Problem list !! Solutions | ! Date !! Summary !! Lecture notes !! Problem list !! Solutions | ||
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| − | | 3 sept || PAC-learning in the realizable setting definitions || | + | | 3 sept || PAC-learning in the realizable setting definitions || [https://www.dropbox.com/s/4ic3ce71znglmu9/01sem.pdf?dl=0 Problem list 1] |
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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. | ||
| − | + | Afterwards, we hope to cover chapters 1-8 from the book: | |
| + | Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012. These books can downloaded from http://gen.lib.rus.ec/ . | ||
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(We will study a new boosting algorithm, based on the paper: [http://www.cs.nyu.edu/~mohri/pub/mboost.pdf Multi-class deep boosting], V. Kuznetsov, M Mohri, and U. Syed, Advances in Neural Information Processing Systems, p2501--2509, 2014. Notes will be provided.) | (We will study a new boosting algorithm, based on the paper: [http://www.cs.nyu.edu/~mohri/pub/mboost.pdf Multi-class deep boosting], V. Kuznetsov, M Mohri, and U. Syed, Advances in Neural Information Processing Systems, p2501--2509, 2014. Notes will be provided.) | ||
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Версия 17:36, 3 сентября 2018
Information
The syllabus
General Information
Course materials
| Date | Summary | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|
| 3 sept | PAC-learning in the realizable setting definitions | Problem list 1 |
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: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.
Afterwards, we hope to cover chapters 1-8 from the book: Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012. These books can downloaded from http://gen.lib.rus.ec/ .
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | ||
|---|---|---|---|---|---|---|---|
| |
Bruno Bauwens | 16:45–19:00 | 15:05–18:00 | Room 620 |
Russian texts
The following links might help students who have trouble with English. A lecture on VC-dimensions was given by K. Vorontsov. A course on Statistical Learning Theory by Nikita Zhivotovsky is given at MIPT. Some short description about PAC learning on p136 in the book ``Наука и искусство построения алгоритмов, которые извлекают знания из данных, Петер Флах. On machinelearning.ru you can find brief and clear definitions.
