Statistical learning theory 2018 2019 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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| − | Deadline homework 1: October 2nd. Questions: see seminars [https://www.dropbox.com/s/jb9mriumhtdpn8m/03sem.pdf?dl=0 3] and | + | Deadline homework 1: October 2nd. Questions: see seminars [https://www.dropbox.com/s/jb9mriumhtdpn8m/03sem.pdf?dl=0 3] and 4. |
Deadline homework 2: October 27nd. | Deadline homework 2: October 27nd. | ||
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| 17 sept || Proof that finite VC-dimension implies PAC-learnability || [https://www.dropbox.com/s/9rfvwvf0ne95j8e/03lect.pdf?dl=0 lecture3.pdf] updated 23/09 || [https://www.dropbox.com/s/jb9mriumhtdpn8m/03sem.pdf?dl=0 Problem list 3] || | | 17 sept || Proof that finite VC-dimension implies PAC-learnability || [https://www.dropbox.com/s/9rfvwvf0ne95j8e/03lect.pdf?dl=0 lecture3.pdf] updated 23/09 || [https://www.dropbox.com/s/jb9mriumhtdpn8m/03sem.pdf?dl=0 Problem list 3] || | ||
| − | + | | 24 sept || Applications to complex classifiers: decision trees and threshold neural networks. PAC-learnability in the agnostic setting. || [ | |
| − | | 24 sept || Applications to decision trees and threshold neural networks | + | |
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Версия 19:28, 25 сентября 2018
General Information
The syllabus
Deadline homework 1: October 2nd. Questions: see seminars 3 and 4.
Deadline homework 2: October 27nd.
Deadline homework 3: TBA.
Intermediate exams: Oktober 29th.
Course materials
| Date | Summary | Lecture notes | Problem list | Solutions | |||
|---|---|---|---|---|---|---|---|
| 3 sept | PAC-learning in the realizable setting definitions | lecture1.pdf updated 23/09 | Problem list 1 | ||||
| 10 sept | VC-dimension and growth functions | lecture2.pdf updated 23/09 | Problem list 2 | ||||
| 17 sept | Proof that finite VC-dimension implies PAC-learnability | lecture3.pdf updated 23/09 | Problem list 3 | 24 sept | Applications to complex classifiers: decision trees and threshold neural networks. PAC-learnability in the agnostic setting. | [ |
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.
Afterward, 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 be 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.
