Statistical learning theory 2021 — различия между версиями

Материал из Wiki - Факультет компьютерных наук
Перейти к: навигация, поиск
 
(не показано 111 промежуточных версии 2 участников)
Строка 6: Строка 6:
 
Teachers: [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens] and [https://www.hse.ru/en/org/persons/225553845 Nikita Lukianenko]  
 
Teachers: [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens] and [https://www.hse.ru/en/org/persons/225553845 Nikita Lukianenko]  
  
Lectures: Saturday 14:40 - 16:00. The lectures are in room R308 (Pokrovkaya) and also streamed [https://us02web.zoom.us/j/82173400975?pwd=L1lhTzFTc2lGem5BVFdRcFEyVUhqZz09  here] in zoom.
+
Lectures: Saturday 14:40 - 16:00. The lectures are [https://us02web.zoom.us/j/82173400975?pwd=L1lhTzFTc2lGem5BVFdRcFEyVUhqZz09  here] in zoom.  
  
Seminars: Tuesday 16:20 - 17:40. The seminars are in room ?? (Pokrovkaya) and also streamed [https://us02web.zoom.us/j/82612783590?pwd=U0FwOUVkRjYzZlF1blc2d1FNT1FZQT09 here] in zoom.
+
Seminars: Tuesday 16:20 - 17:40. The seminars are [https://meet.google.com/ber-yzns-hxz here] in google.meet.
  
 
+
Practical information on a telegram group. [https://t.me/joinchat/IER2-8hc0wUxNDQ0 Join here.]
Practical information on [https://t.me/joinchat/IER2-8hc0wUxNDQ0 telegram group]
+
  
 
The course is similar [http://wiki.cs.hse.ru/Statistical_learning_theory_2020 last year], except for the order of topics and part 3.
 
The course is similar [http://wiki.cs.hse.ru/Statistical_learning_theory_2020 last year], except for the order of topics and part 3.
 +
 +
== Colloquium ==
 +
 +
Saturday December 11
 +
 +
[https://www.dropbox.com/s/u8hyo1omvaoujle/colloqQuest.pdf?dl=0 rules and list of questions] <span style="color:red">updated Nov 27</span>
 +
 +
== Homeworks ==
 +
 +
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. [https://www.dropbox.com/s/taskzhu0nj5motd/scores.ods?dl=0 Results]
 +
 +
Deadline before the lecture, every other lecture.
 +
 +
25 Sept: see problem lists 1 and 2 <br>
 +
09 Oct: see problem lists 3 and 4  <br>
 +
29 Oct: see problem lists 5 and 6 <br> <!-- <span style="color:red">Note that in problem 6.5, k is fixed, It is now stressed (26 Oct). Deadline extension</span> -->
 +
13 Nov: see problem lists 7 and 8 <br>
 +
<span style="color:red">30 Nov, 08:00 [extended]</span>: see problem lists 9 and 10 <span style="color:red">Update 23 Nov: exercises 9.4 and 10.5b.</span>  <br>
 +
<s>11 Dec: see problem lists 11 and 12 </s><br>
  
 
== Course materials ==
 
== Course materials ==
Строка 19: Строка 37:
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
! Date !! Summary !! Lecture notes !! Problem list !! Solutions
+
! Video !! Summary !! Slides !! Lecture notes !! Problem list !! Solutions
 
|-
 
|-
 
|  
 
|  
|| ''Part 1. Online learning'' || || ||
+
|| ''Part 1. Online learning''  
 
|-
 
|-
| 4 Sept  
+
| [https://drive.google.com/file/d/1WL9LSNDD1B_q6LdpfDQ8BPluNfhjWrD9/view?usp=sharing 4 Sept]
|| Introduction, the online mistake bound model, the weighted majority and perceptron algorithms  
+
|| Lecture: philosophy. Seminar: the online mistake bound model, the weighted majority, and perceptron algorithms [https://drive.google.com/drive/folders/1NXiLbhmO2Ml7jFmnLtjqhOgCoHg7yn9T?usp=sharing movies]
||  
+
|| [https://www.dropbox.com/s/uk9awkfa827pmtf/01allSlides.pdf?dl=0 sl01]
||
+
|| [https://www.dropbox.com/s/uvsfzb997kantoa/00book_intro.pdf?dl=0 ch00] [https://www.dropbox.com/s/6ah70h5loyrz5lx/01book_onlineMistakeBound.pdf?dl=0 ch01]
||
+
|| [https://www.dropbox.com/s/aoma8ma8mkd3885/01sem.pdf?dl=0 01prob (9 Sept)]
 +
|| [https://www.dropbox.com/s/sqzqlrtzr2nu8cq/01sol.pdf?dl=0 01sol]
 
|-
 
|-
| 11 Sept
+
| [https://drive.google.com/file/d/16OoCqhh16BKQzyF-HM8RozigyJ3BBVxA/view?usp=sharing 11 Sept]
|| The standard optimal algorithm, prediction with expert advice, exponentially weighted algorithm
+
|| The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm.
||  
+
|| [https://www.dropbox.com/s/sy959ee81mov5cr/02slides.pdf?dl=0 sl02]
||
+
|| [https://www.dropbox.com/s/0029k15cbnxj2v1/02book_sequentialOptimalAlgorithm.pdf?dl=0 ch02] [https://www.dropbox.com/s/eggk7kctgox8aza/03book_perceptron.pdf?dl=0 ch03]
||
+
|| [https://www.dropbox.com/s/415nws7qi589bme/02sem.pdf?dl=0 02prob (23 Sept)]
 +
|| [https://www.dropbox.com/s/ofcctflbnxt0kx3/02sol.pdf?dl=0 02sol]
 
|-
 
|-
| 18 Sept
+
| 18 Sept (rec to do)
|| Better mistake bounds using VC-dimensions. Recap probability theory. Leave on out risk for SVM.
+
|| Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory.  
||
+
|| [https://www.dropbox.com/s/a60p9b76cxusgqy/03slides.pdf?dl=0 sl03]
||
+
|| [https://www.dropbox.com/s/ytl6q83q6gkax3w/04book_predictionWithExperts.pdf?dl=0 ch04] [https://www.dropbox.com/s/l11afq1d0qn6za7/05book_introProbability.pdf?dl=0 ch05]
||
+
|| [https://www.dropbox.com/s/nsrcy3yxgey67lp/03sem.pdf?dl=0 03prob(30 Sept)]
 +
|| [https://www.dropbox.com/s/bg9nd01h1fhzjsi/03sol.pdf?dl=0 03sol]
 
|-
 
|-
 
|  
 
|  
|| ''Part 2. Supervised classification'' || || ||
+
|| ''Part 2. Risk bounds for binary classification''  
 
|-
 
|-
| 25 Sept
+
| [https://drive.google.com/file/d/1RHz8NgfianUQFlx8VswjiiPRvt0DoBvc/view?usp=sharing 25 Sept]
|| Sample complexity in the realizable setting, simple example and bounds using VC-dimension
+
|| Sample complexity in the realizable setting, simple examples and bounds using VC-dimension
||
+
|| [https://www.dropbox.com/s/pi0f3wab1xna6d7/04slides.pdf?dl=0 sl04]
||
+
|| [https://www.dropbox.com/s/8xrgcugs4xv2r2p/06book_sampleComplexity.pdf?dl=0 ch06]
||
+
|| [https://www.dropbox.com/s/7qn2yz5fxc93rez/04sem.pdf?dl=0 04prob]
 +
|| [https://www.dropbox.com/s/xm3nhgj5d6h49nz/04sol.pdf?dl=0 04sol]
 
|-  
 
|-  
| 2 Oct
+
| [https://drive.google.com/drive/folders/1jjyJ3eIaed64ogpR11g8M44IOikt5Mj2?usp=sharing 2 Oct]
 +
|| Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions
 +
|| [https://www.dropbox.com/s/rpnh6288rdb3j8m/05slides.pdf?dl=0 sl05]
 +
|| [https://www.dropbox.com/s/ctc48w1d2vvyiyt/07book_growthFunctions.pdf?dl=0 ch07] [https://www.dropbox.com/s/jofixf9tstz0f8z/08book_VCdimension.pdf?dl=0 ch08]
 +
|| [https://www.dropbox.com/s/zbyqxy3qp3pz79i/05sem.pdf?dl=0 05prob]
 +
|| [https://www.dropbox.com/s/a8efm18dof2zeox/05sol.pdf?dl=0 05sol]
 +
|-
 +
| [https://drive.google.com/file/d/17zynIg_CZ6cCNBig5QXmBx7VFS8peyuU/view?usp=sharing 9 Oct]
 
|| Risk decomposition and the fundamental theorem of statistical learning theory
 
|| Risk decomposition and the fundamental theorem of statistical learning theory
||
+
|| [https://www.dropbox.com/s/jxijka88vfanv5n/06slides.pdf?dl=0 sl06]
||
+
|| [https://www.dropbox.com/s/r44bwxz34qj98gg/09book_riskBounds.pdf?dl=0 ch09]
||
+
|| [https://www.dropbox.com/s/x87txc8v5v6u8vb/06sem.pdf?dl=0 06prob]
|-
+
|| [https://www.dropbox.com/s/ydlqu8oce3xj6ix/06sol.pdf?dl=0 06sol]
| 9 Oct
+
|| Rademacher complexity
+
||
+
||
+
||
+
 
|-
 
|-
 
| 16 Oct
 
| 16 Oct
|| Support vector machines and margin risk bounds
+
|| Bounded differences inequality and Rademacher complexity
||  
+
|| [https://www.dropbox.com/s/kfithyq0dgcq6h8/07slides.pdf?dl=0 sl07]
||
+
|| [https://www.dropbox.com/s/5quc1jfkrvm3t71/10book_measureConcentration.pdf?dl=0 ch10] [https://www.dropbox.com/s/km0fns8n3aihauv/11book_RademacherComplexity.pdf?dl=0 ch11]
||
+
|| [https://www.dropbox.com/s/d1rsxceqmbk5llw/07sem.pdf?dl=0 07prob]
 +
|| [https://www.dropbox.com/s/sftaa8b92ru3ii5/07sol.pdf?dl=0 07sol]
 
|-
 
|-
| 29 Oct
+
| [https://drive.google.com/file/d/1L-BeDxhoHcoDrdlVTlfoMFwnWXKV46cr/view?usp=sharing 30 Oct]
|| Kernels: risk bounds, design, and representer theorem
+
|| Simple regression, support vector machines, margin risk bounds, and neural nets
||
+
|| [https://www.dropbox.com/s/0xrhe4732d0jshb/08slides.pdf?dl=0 sl08]
||
+
|| [https://www.dropbox.com/s/cvqlwst3e69709t/12book_regression.pdf?dl=0 ch12] [https://www.dropbox.com/s/dwwxgriiaj4efn0/13book_SVM.pdf?dl=0 ch13]
||
+
|| [https://www.dropbox.com/s/qqdbrh2ll0dv03a/08sem.pdf?dl=0 08prob]
 +
|| [https://www.dropbox.com/s/9o8fyd0ff735hxu/08sol.pdf?dl=0 08sol]
 +
|-
 +
| [https://youtu.be/9FhFxLHR4eE 6 Nov]
 +
|| Kernels: risk bounds, RKHS, representer theorem, design
 +
|| [https://www.dropbox.com/s/nhqtbekclekf6k7/09slides.pdf?dl=0 sl09]
 +
|| [https://www.dropbox.com/s/bpb9ijn2p7k19j3/14book_kernels.pdf?dl=0 ch14]
 +
|| [https://www.dropbox.com/s/d2dmh017lw207ns/09sem.pdf?dl=0 09prob] (Nov 23)
 +
|| [https://www.dropbox.com/s/2wq9mxrqchsqujr/09sol.pdf?dl=0 09sol]
 
|-  
 
|-  
| 6 Nov
+
| [https://youtu.be/ZBHe5RhTuzI 13 Nov]
 
|| AdaBoost and risk bounds
 
|| AdaBoost and risk bounds
||
+
|| [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10]
||
+
|| Mohri et al, chapt 7
||
+
|| [https://www.dropbox.com/s/j8s197e0mjv9qla/10sem.pdf?dl=0 10prob] (Nov 23)
 +
|| [https://www.dropbox.com/s/7lw1u8750k7s8qt/10sol.pdf?dl=0 10sol]
 
|-
 
|-
 
|
 
|
|| ''Part 3. Other topics'' || || ||
+
|| ''Part 3. Other topics''  
 
|-
 
|-
| 13 Nov  
+
| [https://youtu.be/L4o7dXcaQrk 20 Nov]
 
|| Clustering   
 
|| Clustering   
||  
+
|| [https://www.dropbox.com/s/5a9flvg95iihz7m/11slides.pdf?dl=0 sl11]
||
+
|| Mohri et al, ch7; [https://people.csail.mit.edu/dsontag/courses/ml12/slides/lecture14.pdf lecture]
||  
+
|| [https://www.dropbox.com/s/a9459keof3omav1/11sem.pdf?dl=0 11prob]
 +
|| [https://www.dropbox.com/s/kredac52pbn7qvk/11sol.pdf?dl=0 11sol]
 
|-
 
|-
| 20 Nov
+
| [https://youtu.be/FN6l4Ceq5lE 27 Nov]
 
|| Dimensionality reduction and the Johnson-Lindenstrauss lemma
 
|| Dimensionality reduction and the Johnson-Lindenstrauss lemma
||
+
|| [https://www.dropbox.com/s/wbgwwk7a9mjo1bv/12slides.pdf?dl=0 sl12]
 +
|| Mohri et al, ch15; [https://ramanlab.wustl.edu/Lectures/Lecture12_LDA_CCA.pdf lecture]
 
||
 
||
 
||
 
||
 
|-
 
|-
| 27 Nov
+
| 4 Dec
|| Active learning
+
|| No lecture
 
||
 
||
||
 
||
 
|-
 
| 4 Dec
 
|| Extra space for a lesson, in the likely case we are a bit slower.
 
 
||
 
||
 
||
 
||
Строка 110: Строка 141:
 
| 11 Dec
 
| 11 Dec
 
|| Colloquium  
 
|| Colloquium  
 +
||
 
||
 
||
 
||
 
||
Строка 206: Строка 238:
 
The lectures in October and November are based on the book:
 
The lectures in October and November are based on the book:
 
Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. This book can be downloaded from http://gen.lib.rus.ec/ .
 
Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. This book can be downloaded from http://gen.lib.rus.ec/ .
 
  
 
== Office hours ==
 
== Office hours ==
Строка 215: Строка 246:
 
|-
 
|-
 
|  [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] ||  || 12h30-14h30 || || || 14h-20h || Room&nbsp;S834 Pokrovkaya 11
 
|  [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens], [https://zoom.us/j/5579743402 Zoom] ||  || 12h30-14h30 || || || 14h-20h || Room&nbsp;S834 Pokrovkaya 11
 +
|-
 +
|  [https://www.hse.ru/org/persons/225553845 Nikita Lukianenko], [https://t.me/vaulty Telegram] ||  || 14h30-16h30 || 14h30-16h30 || || || Room&nbsp;S831 Pokrovkaya 11
 
|-
 
|-
 
|}
 
|}
  
It is always good to send an email in advance. Questions and feedback are welcome.
+
It is always good to send an email in advance. Questions and feedback are welcome.
 +
 
 +
I am traveling from Sept 12 -- Sept 30 and Oct 16 -- Oct 26. On Fridays I'm available till 16h30.  
  
 
<!--
 
<!--

Текущая версия на 21:37, 27 ноября 2021

General Information

Grading

Teachers: Bruno Bauwens and Nikita Lukianenko

Lectures: Saturday 14:40 - 16:00. The lectures are here in zoom.

Seminars: Tuesday 16:20 - 17:40. The seminars are here in google.meet.

Practical information on a telegram group. Join here.

The course is similar last year, except for the order of topics and part 3.

Colloquium

Saturday December 11

rules and list of questions updated Nov 27

Homeworks

Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. Results

Deadline before the lecture, every other lecture.

25 Sept: see problem lists 1 and 2
09 Oct: see problem lists 3 and 4
29 Oct: see problem lists 5 and 6
13 Nov: see problem lists 7 and 8
30 Nov, 08:00 [extended]: see problem lists 9 and 10 Update 23 Nov: exercises 9.4 and 10.5b.
11 Dec: see problem lists 11 and 12

Course materials

Video Summary Slides Lecture notes Problem list Solutions
Part 1. Online learning
4 Sept Lecture: philosophy. Seminar: the online mistake bound model, the weighted majority, and perceptron algorithms movies sl01 ch00 ch01 01prob (9 Sept) 01sol
11 Sept The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm. sl02 ch02 ch03 02prob (23 Sept) 02sol
18 Sept (rec to do) Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory. sl03 ch04 ch05 03prob(30 Sept) 03sol
Part 2. Risk bounds for binary classification
25 Sept Sample complexity in the realizable setting, simple examples and bounds using VC-dimension sl04 ch06 04prob 04sol
2 Oct Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions sl05 ch07 ch08 05prob 05sol
9 Oct Risk decomposition and the fundamental theorem of statistical learning theory sl06 ch09 06prob 06sol
16 Oct Bounded differences inequality and Rademacher complexity sl07 ch10 ch11 07prob 07sol
30 Oct Simple regression, support vector machines, margin risk bounds, and neural nets sl08 ch12 ch13 08prob 08sol
6 Nov Kernels: risk bounds, RKHS, representer theorem, design sl09 ch14 09prob (Nov 23) 09sol
13 Nov AdaBoost and risk bounds sl10 Mohri et al, chapt 7 10prob (Nov 23) 10sol
Part 3. Other topics
20 Nov Clustering sl11 Mohri et al, ch7; lecture 11prob 11sol
27 Nov Dimensionality reduction and the Johnson-Lindenstrauss lemma sl12 Mohri et al, ch15; lecture
4 Dec No lecture
11 Dec Colloquium


The lectures in October and November are based on the book: Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. This book can be downloaded from http://gen.lib.rus.ec/ .

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens, Zoom 12h30-14h30 14h-20h Room S834 Pokrovkaya 11
Nikita Lukianenko, Telegram 14h30-16h30 14h30-16h30 Room S831 Pokrovkaya 11

It is always good to send an email in advance. Questions and feedback are welcome.

I am traveling from Sept 12 -- Sept 30 and Oct 16 -- Oct 26. On Fridays I'm available till 16h30.