Statistical learning theory 2018 2019: различия между версиями

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== General Information ==
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The [https://www.dropbox.com/s/8iivgt3a96yw308/syllabus_StatisticalLearning_Bach_2018_2019.pdf?dl=0 syllabus]
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[https://www.dropbox.com/s/dh2n9cmhpmx97nj/colloqQuest.pdf?dl=0 Questions colloquium on 29 October.] (Lectures 1-8 updated 24/10.)
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Deadline homework 1: October 2nd. Questions: see seminars [https://www.dropbox.com/s/jb9mriumhtdpn8m/03sem.pdf?dl=0 3] and [https://www.dropbox.com/s/l2d9f7u77smrx4u/04sem.pdf?dl=0 4].
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Deadline homework 2: October 27nd. Questions: see seminars 5-8 below.
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Deadline homework 3: December 11nd. Questions: see seminars 9-12 below.
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[https://www.dropbox.com/s/dy9yu1ro4k5miet/List%20of%20Students_Bruno.xlsx?dl=0  Marks]
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Intermediate exams: October 29th.
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Final exam: December 20th, same system as for intermediate exams. [https://www.dropbox.com/s/uaxdredmmm5ke7t/finalTheoryQuest.pdf?dl=0 Theory questions]
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Consultation: December 17th, no lecture. Students can ask questions and ask for solutions of exercises.
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== Course materials ==
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{| class="wikitable"
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! Date !! Summary !! Lecture notes !! Problem list !! Solutions
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|-
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| 3 Sept || PAC-learning in the realizable setting definitions  || [https://www.dropbox.com/s/l8e8xjfe2f8tjz8/01lect.pdf?dl=0 lecture1.pdf] updated 23/09
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|| [https://www.dropbox.com/s/4ic3ce71znglmu9/01sem.pdf?dl=0 Problem list 1] || [https://www.dropbox.com/s/cixli4sghy0w01q/01solution.pdf?dl=0 Solutions 1]
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| 10 Sept || VC-dimension and growth functions || [https://www.dropbox.com/s/q1jc2dlotwdn9e2/02lect.pdf?dl=0 lecture2.pdf] updated 23/09 || [https://www.dropbox.com/s/4gimo3fij5p7lnc/02sem.pdf?dl=0 Problem list 2] || [https://www.dropbox.com/s/69pnkefexsmq6nu/02solution.pdf?dl=0 Solutions 2]
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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] || [https://www.dropbox.com/s/f0gnrfxv9i7at91/03solution.pdf?dl=0 Solutions 3]
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| 24 Sept || Applications to decision trees and threshold neural networks. Agnostic PAC-learnability. || [https://www.dropbox.com/s/9oa2zg7jz2ovquf/04lect.pdf?dl=0 lecture4.pdf] || [https://www.dropbox.com/s/l2d9f7u77smrx4u/04sem.pdf?dl=0  Problem list 4] || [https://www.dropbox.com/s/t4tqtw7tdh54u2i/04solution.pdf?dl=0 Solution 4]
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| 1 Oct || Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization || [https://www.dropbox.com/s/jsrse5qaqk2jhi1/05lect.pdf?dl=0 lecture5.pdf] 14/10 || [https://www.dropbox.com/s/etw67uq1pu5g58t/05sem.pdf?dl=0 Problem list 5] || [https://www.dropbox.com/s/6mpom53yrldcrjy/05solution.pdf?dl=0 Solution 5]
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| 9 Oct || Boosting, Mohri's book pages 121-131. || [https://www.dropbox.com/s/m6tc4miryv6cs21/06lect.pdf?dl=0 lecture6.pdf] 23/10 || [https://www.dropbox.com/s/85t74k9wmibcnmr/06sem.pdf?dl=0 Problem list 6] || No solution.
|-
| 15 Oct || Rademacher complexity and contraction lemma (=Talagrand's lemma), Mohri's book pages 33-41 and 78-79 || [https://www.dropbox.com/s/y2vr3mrwp66cuvz/07lect.pdf?dl=0 lecture7.pdf] || [https://www.dropbox.com/s/cuo0tmfv4k2egvh/07sem.pdf?dl=0 Problem list 7] || See lecture7.pdf
|-
| 21 Oct || Margin theory and risk bounds for boosting. || [https://www.dropbox.com/s/o5zae3d8nw5eexw/08lect.pdf?dl=0 lecture8.pdf] || [https://www.dropbox.com/s/xg7u3ss1a0vog5j/08sem.pdf?dl=0 Problem list 8]|| See lecture6.pdf for ex. 8.6.
|-
| 12 Nov || Deep boosting, we study 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. || [https://www.dropbox.com/s/tc7drmxwu53opzq/09lect.pdf?dl=0 lecture9.pdf] || [https://www.dropbox.com/s/lsu6tgmc767u3yd/09sem.pdf?dl=0 Problem list 9] || [https://www.dropbox.com/s/8wmswbynzx0s9hd/09sol.pdf?dl=0 Solutions 9.]
|-
| 19 Nov || Support vector machines, primal and dual optimization problem, risk bounds.  || See chapt. 5 of Mohri's book || [https://www.dropbox.com/s/ys37nsdfz3aa4ry/10sem.pdf?dl=0 Problem list 10]|| No solution.
|-
| 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
|-
| 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].  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/aq6798jps111l86/12lect.pdf?dl=0 lecture12.pdf] || [https://www.dropbox.com/s/o4t6smc70o1bt3t/12sem.pdf?dl=0 Problem list 12] || No solution.
|-
| 10 Dec || We finish online learning. Discuss the algorithm from [http://papers.nips.cc/paper/4616-bandit-algorithms-boost-brain-computer-interfaces-for-motor-task-selection-of-a-brain-controlled-button.pdf this paper].  || || See previous list. ||
|}
 
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/ .
 
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== Office hours ==
 
{| class="wikitable"
|-
! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !!
|-
|  Bruno Bauwens ||  16:45&ndash;19:00 || 15:05&ndash;18:00 || || ||  || Room&nbsp;620
|-
|}
 
 
== Russian texts  ==
 
The following links might help students who have trouble with English.  A [http://www.machinelearning.ru/wiki/images/d/d9/Voron-2011-tnop.pdf  lecture] on VC-dimensions was given by K. Vorontsov.
A [http://machinelearning.ru/wiki/index.php?title=%D0%A2%D0%B5%D0%BE%D1%80%D0%B8%D1%8F_%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8F_(%D0%BA%D1%83%D1%80%D1%81_%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D0%B9%2C_%D0%9D._%D0%9A._%D0%96%D0%B8%D0%B2%D0%BE%D1%82%D0%BE%D0%B2%D1%81%D0%BA%D0%B8%D0%B9) course] on Statistical Learning Theory by Nikita Zhivotovsky is given at MIPT. Some short description about PAC learning on p136 in the [http://gen.lib.rus.ec/search.php?req=%D0%9D%D0%B0%D1%83%D0%BA%D0%B0+%D0%B8+%D0%B8%D1%81%D0%BA%D1%83%D1%81%D1%81%D1%82%D0%B2%D0%BE+%D0%BF%D0%BE%D1%81%D1%82%D1%80%D0%BE%D0%B5%D0%BD%D0%B8%D1%8F+%D0%B0%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC%D0%BE%D0%B2%2C+%D0%BA%D0%BE%D1%82%D0%BE%D1%80%D1%8B%D0%B5+%D0%B8%D0%B7%D0%B2%D0%BB%D0%B5%D0%BA%D0%B0%D1%8E%D1%82+%D0%B7%D0%BD%D0%B0%D0%BD%D0%B8%D1%8F+%D0%B8%D0%B7+%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85&lg_topic=libgen&open=0&view=simple&res=25&phrase=0&column=def book]
``Наука и искусство построения алгоритмов, которые извлекают знания из данных'', Петер Флах. On [http://www.machinelearning.ru machinelearning.ru]
you can find brief and clear definitions.

Версия от 23:27, 17 августа 2022

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Источник — https://wiki.cs.hse.ru/index.php?title=Statistical_learning_theory_2018_2019&oldid=71098