Statistical learning theory 2018 2019 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
||
Строка 11: | Строка 11: | ||
! Date !! Summary !! Lecture notes !! Problem list !! Solutions | ! Date !! Summary !! Lecture notes !! Problem list !! Solutions | ||
|- | |- | ||
− | | 3 sept || PAC-learning in the realizable setting definitions || [https://www.dropbox.com/s/l8e8xjfe2f8tjz8/01lect.pdf?dl=0 lecture1.pdf] | + | | 3 sept || PAC-learning in the realizable setting definitions || [https://www.dropbox.com/s/l8e8xjfe2f8tjz8/01lect.pdf?dl=0 lecture1.pdf update 14/09] |
|| [https://www.dropbox.com/s/4ic3ce71znglmu9/01sem.pdf?dl=0 Problem list 1] || | || [https://www.dropbox.com/s/4ic3ce71znglmu9/01sem.pdf?dl=0 Problem list 1] || | ||
|} | |} |
Версия 21:46, 14 сентября 2018
General Information
The syllabus
Course materials
Date | Summary | Lecture notes | Problem list | Solutions |
---|---|---|---|---|
3 sept | PAC-learning in the realizable setting definitions | lecture1.pdf update 14/09 | 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.