Statistical learning theory 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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Deadline before the lecture, every other lecture. | Deadline before the lecture, every other lecture. | ||
| − | + | 16 Sept: see problem lists 1 and 2 <br> | |
| − | + | 30 Sept: see problem lists 3 and 4 <br> | |
| − | + | 14 Oct: see problem lists 5 and 6 <br> | |
| − | 04 Nov: see problem list 7 <br> | + | 04 Nov: see problem list 7 and 8 <br> |
| − | + | 28 Nov: see problem lists 9 and 10 <br> | |
| − | 02 Dec: see problem lists | + | 02 Dec: see problem lists 11 and 12 <br> |
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|| [https://www.dropbox.com/s/uk9awkfa827pmtf/01allSlides.pdf?dl=0 sl01] | || [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/uvsfzb997kantoa/00book_intro.pdf?dl=0 ch00] [https://www.dropbox.com/s/6ah70h5loyrz5lx/01book_onlineMistakeBound.pdf?dl=0 ch01] | ||
| − | || | + | || problem list 1 |
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|| [https://www.dropbox.com/s/sy959ee81mov5cr/02slides.pdf?dl=0 sl02] | || [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/0029k15cbnxj2v1/02book_sequentialOptimalAlgorithm.pdf?dl=0 ch02] [https://www.dropbox.com/s/eggk7kctgox8aza/03book_perceptron.pdf?dl=0 ch03] | ||
| − | || | + | || problem list 2 |
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|| [https://www.dropbox.com/s/a60p9b76cxusgqy/03slides.pdf?dl=0 sl03] | || [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/ytl6q83q6gkax3w/04book_predictionWithExperts.pdf?dl=0 ch04] [https://www.dropbox.com/s/l11afq1d0qn6za7/05book_introProbability.pdf?dl=0 ch05] | ||
| − | || | + | || problem list 3 |
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| Строка 63: | Строка 63: | ||
|| [https://www.dropbox.com/s/pi0f3wab1xna6d7/04slides.pdf?dl=0 sl04] | || [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/8xrgcugs4xv2r2p/06book_sampleComplexity.pdf?dl=0 ch06] | ||
| − | || | + | || problem list 4 |
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| Строка 70: | Строка 70: | ||
|| [https://www.dropbox.com/s/rpnh6288rdb3j8m/05slides.pdf?dl=0 sl05] | || [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/ctc48w1d2vvyiyt/07book_growthFunctions.pdf?dl=0 ch07] [https://www.dropbox.com/s/jofixf9tstz0f8z/08book_VCdimension.pdf?dl=0 ch08] | ||
| − | || | + | || problem list 5 |
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| Строка 77: | Строка 77: | ||
|| [https://www.dropbox.com/s/jxijka88vfanv5n/06slides.pdf?dl=0 sl06] | || [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/r44bwxz34qj98gg/09book_riskBounds.pdf?dl=0 ch09] | ||
| − | || | + | || problem list 6 |
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|| [https://www.dropbox.com/s/kfithyq0dgcq6h8/07slides.pdf?dl=0 sl07] | || [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/5quc1jfkrvm3t71/10book_measureConcentration.pdf?dl=0 ch10] [https://www.dropbox.com/s/km0fns8n3aihauv/11book_RademacherComplexity.pdf?dl=0 ch11] | ||
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|| [https://www.dropbox.com/s/0xrhe4732d0jshb/08slides.pdf?dl=0 sl08] | || [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/cvqlwst3e69709t/12book_regression.pdf?dl=0 ch12] [https://www.dropbox.com/s/dwwxgriiaj4efn0/13book_SVM.pdf?dl=0 ch13] | ||
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|| [https://www.dropbox.com/s/nhqtbekclekf6k7/09slides.pdf?dl=0 sl09] | || [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/bpb9ijn2p7k19j3/14book_kernels.pdf?dl=0 ch14] | ||
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|| [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | || [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | ||
|| Mohri et al, chapt 7 | || Mohri et al, chapt 7 | ||
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Версия 17:23, 30 августа 2022
General Information
Lectures: Friday 16h20 -- 17h40, Bruno Bauwens, Maxim Kaledin
Seminars: Friday 18h10 -- 19h30, Artur Goldman,
For discussions of the materials, join the telegram group
The course is similar to last year.
Homeworks
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW.
Deadline before the lecture, every other lecture.
16 Sept: see problem lists 1 and 2
30 Sept: see problem lists 3 and 4
14 Oct: see problem lists 5 and 6
04 Nov: see problem list 7 and 8
28 Nov: see problem lists 9 and 10
02 Dec: see problem lists 11 and 12
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 02 Sept | Philosophy. The online mistake bound model. Weighted majority and perceptron algorithms movies | sl01 | ch00 ch01 | problem list 1 | |
| 09 Sept | The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm. | sl02 | ch02 ch03 | problem list 2 | |
| 16 Sept | Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory. | sl03 | ch04 ch05 | problem list 3 | |
| Part 2. Distribution independent risk bounds | |||||
| 23 Sept | Sample complexity in the realizable setting, simple examples and bounds using VC-dimension | sl04 | ch06 | problem list 4 | |
| 30 Sept | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | problem list 5 | |
| 07 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | sl06 | ch09 | problem list 6 | |
| 14 Oct | Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma | sl07 | ch10 ch11 | problem list 7 | |
| Part 3. Margin risk bounds with applications | |||||
| 21 Oct | Simple regression, support vector machines, margin risk bounds, and neural nets | sl08 | ch12 ch13 | problem list 8 | |
| 04 Nov | Kernels: RKHS, representer theorem, risk bounds | sl09 | ch14 | problem list 9 | |
| 11 Nov | AdaBoost and the margin hypothesis | sl10 | Mohri et al, chapt 7 | problem list 10 | |
| 18 Nov | Implicit regularization of stochastic gradient descent in neural nets | problem list 11 | |||
| Part 4. Other topics | |||||
| 25 Nov | Regression I: classic noise assumption, sub-Guassian and sub-exponential noise | problem list 12 | |||
| 02 Dec | Regression II: Ridge and Lasso regression | problem list 13 | |||
| 09 Dec | Multiarmed bandids | problem list 14 | |||
| 16 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/ .
Problems exam
Dates, problems TBA
During the exam
-- You may consult notes, books and search on the internet
-- You may not interact with other humans (e.g. by phone, forums, etc)
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| Bruno Bauwens | 14h--20h | |||||
| Maxim Kaledin |
It is always good to send an email in advance. Questions and feedback are welcome.
