Statistical learning theory 2018 2019 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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| 26 Nov || Kernels, Kernel reproducing Hilbert spaces, representer theorem, examples of kernels || [https://www.dropbox.com/s/xkic1j6r516ierl/11lect.pdf?dl=0 lecture11.pdf] || [https://www.dropbox.com/s/g3huq5aqzdaesrg/11sem.pdf?dl=0 Problem set 11] || Solutions: see lecture11.pdf | | 26 Nov || Kernels, Kernel reproducing Hilbert spaces, representer theorem, examples of kernels || [https://www.dropbox.com/s/xkic1j6r516ierl/11lect.pdf?dl=0 lecture11.pdf] || [https://www.dropbox.com/s/g3huq5aqzdaesrg/11sem.pdf?dl=0 Problem set 11] || Solutions: see lecture11.pdf | ||
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| − | | 3 Dec || A polynomial time improper learning algorithm for constant depth L1-regularized neural networks. Online algorithms: halving algorithm, weighted and exponentially weighted average algorithms. See Mohri's book Sections 7.1 and 7.2. || || [https://www.dropbox.com/s/o4t6smc70o1bt3t/12sem.pdf?dl=0 Problem list 12] || | + | | 3 Dec || A polynomial time improper learning algorithm for constant depth L1-regularized neural networks, from [http://www.jmlr.org/proceedings/papers/v48/zhangd16.pdf this paper]. [Notes will be written soon.] Online algorithms: halving algorithm, weighted and exponentially weighted average algorithms. See Mohri's book Sections 7.1 and 7.2. || || [https://www.dropbox.com/s/o4t6smc70o1bt3t/12sem.pdf?dl=0 Problem list 12] || |
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Версия 17:25, 4 декабря 2018
General Information
The syllabus
Questions colloquium on 29 October. (Lectures 1-8 updated 24/10.)
Deadline homework 1: October 2nd. Questions: see seminars 3 and 4.
Deadline homework 2: October 27nd. Questions: see seminars 5-8 below.
Deadline homework 3: December 11nd. Questions: see seminars 9-12 below.
Intermediate exams: October 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 | Solutions 1 |
| 10 Sept | VC-dimension and growth functions | lecture2.pdf updated 23/09 | Problem list 2 | Solutions 2 |
| 17 Sept | Proof that finite VC-dimension implies PAC-learnability | lecture3.pdf updated 23/09 | Problem list 3 | Solutions 3 |
| 24 Sept | Applications to decision trees and threshold neural networks. Agnostic PAC-learnability. | lecture4.pdf | Problem list 4 | Solution 4 |
| 1 Oct | Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization | lecture5.pdf 14/10 | Problem list 5 | Solution 5 |
| 9 Oct | Boosting, Mohri's book pages 121-131. | lecture6.pdf 23/10 | Problem list 6 | No solution. |
| 15 Oct | Rademacher complexity and contraction lemma (=Talagrand's lemma), Mohri's book pages 33-41 and 78-79 | lecture7.pdf | Problem list 7 | See lecture7.pdf |
| 21 Oct | Margin theory and risk bounds for boosting. | lecture8.pdf | Problem list 8 | See lecture6.pdf for ex. 8.6. |
| 12 Nov | Deep boosting, we study the paper Multi-class deep boosting, V. Kuznetsov, M Mohri, and U. Syed, Advances in Neural Information Processing Systems, p2501--2509, 2014. Notes will be provided. | Problem list 9 | ||
| 19 Nov | Support vector machines, primal and dual optimization problem, risk bounds. | See chapt. 5 of Mohri's book | Problem list 10 | |
| 26 Nov | Kernels, Kernel reproducing Hilbert spaces, representer theorem, examples of kernels | lecture11.pdf | Problem set 11 | Solutions: see lecture11.pdf |
| 3 Dec | A polynomial time improper learning algorithm for constant depth L1-regularized neural networks, from this paper. [Notes will be written soon.] Online algorithms: halving algorithm, weighted and exponentially weighted average algorithms. See Mohri's book Sections 7.1 and 7.2. | Problem list 12 |
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.
