Statistical learning theory 2024/25 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Bauwens (обсуждение | вклад) |
Bauwens (обсуждение | вклад) |
||
| (не показано 37 промежуточных версии 2 участников) | |||
| Строка 1: | Строка 1: | ||
== General Information == | == General Information == | ||
| − | Lectures: on | + | Lectures: on Tuesday 9h30--10h50 in room M302 and in [https://us02web.zoom.us/j/82300259484?pwd=NWxXekxBeE5yMm9UTmwvLzNNNGlnUT09 zoom] by [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens] |
| − | Seminars: | + | Seminars: online in [https://us06web.zoom.us/j/85239566702?pwd=y4uhpPrdjSVKOS2LkDIcKCzBXtCbFb.1 Zoom] by [https://www.hse.ru/org/persons/225553845/ Nikita Lukianenko]. |
| − | + | Please join the [https://t.me/+1begXb8SomhmODI8 telegram group] The course is similar to [http://wiki.cs.hse.ru/Statistical_learning_theory_2023/24 last year]. | |
== Homeworks == | == Homeworks == | ||
| Строка 11: | Строка 11: | ||
Deadline every 2 weeks, before the lecture. The tasks are at the end of each problem list. (Problem lists will be updated, check the year.) | Deadline every 2 weeks, before the lecture. The tasks are at the end of each problem list. (Problem lists will be updated, check the year.) | ||
| − | Before 3rd lecture | + | Before 3rd lecture, submit HW from problem lists 1 and 2. |
| − | Before 5th lecture | + | Before 5th lecture, from lists 3 and 4. Etc. |
| − | Etc. | + | |
| − | + | [https://classroom.google.com/c/NzE5NzA4OTg1ODA4?cjc=imgrl43 Classroom] to submit homeworks. You may submit in English or Russian, as latex or as pictures. Results [https://docs.google.com/spreadsheets/d/1k9hivwCzCp3YcR-1n4WnQow94zyQBCjVcgWYaq-PHx8/edit?usp=sharing are here]. | |
| − | Late policy: 1 homework can be submitted at most 24 late without explanations. | + | Late policy: 1 homework can be submitted at most 24 late without explanations. |
== Course materials == | == Course materials == | ||
| Строка 28: | Строка 27: | ||
|| ''Part 1. Online learning'' | || ''Part 1. Online learning'' | ||
|- | |- | ||
| − | | [https://www.youtube.com/watch?v= | + | | [https://www.youtube.com/watch?v=N_JUBxw3sZo 21 Sep] |
| − | || Philosophy. The online mistake bound model. The halving and weighted majority algorithms. | + | || Philosophy. The online mistake bound model. The halving and weighted majority algorithms. |
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/nbxsehlcl8hqodcaho7sg/01slides_all.pdf?rlkey=7u4smvn3jaofhscwrddh6mcoy&st=yb9esz0d&dl=0 sl01] |
| − | || [https://www.dropbox.com/ | + | || [https://www.dropbox.com/scl/fi/svgelu3iwijls092ehqqf/00book_intro.pdf?rlkey=jxdya4290kfc0hfl06b0y7k4b&st=lnv8chxf&dl=0 ch00] [https://www.dropbox.com/scl/fi/uqa9615215wy7ievgr50y/01book_onlineMistakeBound.pdf?rlkey=jiqzz84b5ipaw4t6cff7b17sl&st=mc354l04&dl=0 ch01] |
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/luee4if0mrd4f440q69hd/01sem.pdf?rlkey=8702taq325mvb4ifh15stvvto&st=sq946cf3&dl=0 prob01] |
| − | || | + | || [https://www.dropbox.com/scl/fi/kswtqmyxw3pv336g1vdd6/01sol.pdf?rlkey=bpwnrcsj6ru3nbo4xwq2lp6g0&st=hftnu87m&dl=0 sol01] |
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=gQm1G3Ep-5s | + | | [https://www.youtube.com/watch?v=gQm1G3Ep-5s 24 Sep] |
| − | || The | + | || The standard optimal algorithm. The perceptron algorithm. |
|| [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/ | + | || [https://www.dropbox.com/scl/fi/9016w6j87oclagapah8dt/02book_sequentialOptimalAlgorithm.pdf?rlkey=r729ir0a47ncqip8rooq9txxo&st=zx2tu8gp&dl=0 ch02] [https://www.dropbox.com/scl/fi/iwclbc321iv4k9fmljwpb/03book_perceptron.pdf?rlkey=9v27bt1b9qc2q382l6lwyrkic&st=ni0n8482&dl=0 ch03] |
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/darlkflu0p8idh1smsvqc/02sem.pdf?rlkey=9rxky51dscu0d1pvh0h3iun1i&st=whkfpp78&dl=0 prob02] |
| − | || | + | || [https://www.dropbox.com/scl/fi/d2wuka77bu18j9plivwl5/02sol.pdf?rlkey=yp2eprgxpc7r2antyidjd8qiw&dl=0 sol02] |
|- | |- | ||
| − | | [https://www.youtube.com/watch?v= | + | | [https://www.youtube.com/watch?v=Fk1-QI9PRAI 01 Oct] |
| − | || Prediction with expert advice. Recap probability theory (seminar). | + | || Kernel perceptron algorithm. Prediction with expert advice. Recap probability theory (seminar). |
|| [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/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/7pn3dyf2890p9zuyxleyl/04book_predictionWithExperts.pdf?rlkey=0capmeeu6pwp9wz2mhi0t5h58&st=f4c4n9wo&dl=0 ch04] [https://www.dropbox.com/scl/fi/cx7hsxzwg2f8ep4qcuefc/05book_introProbability.pdf?rlkey=rfq0y9cgzqvl1dlxkccc3qebv&dl=0 ch05] |
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/bkuydm0u3xonnld8qlbl3/03sem.pdf?rlkey=xg2e9sbpe8c2071pxgcohlcab&st=ezxf2zgq&dl=0 prob03] |
| − | || | + | || [https://www.dropbox.com/scl/fi/wjksi4t5r4ng894uiaj8b/03sol.pdf?rlkey=madshl3vupmwkuyzs44ut23ry&st=caroyl3r&dl=0 sol03] |
|- | |- | ||
| | | | ||
|| ''Part 2. Distribution independent risk bounds'' | || ''Part 2. Distribution independent risk bounds'' | ||
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=ycfYXvmKF0I | + | | [https://www.youtube.com/watch?v=ycfYXvmKF0I 08 Oct] |
|| Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. | || Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. | ||
|| [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/nh4puyv7nst4ems/06book_sampleComplexity.pdf?dl=0 ch06] | || [https://www.dropbox.com/s/nh4puyv7nst4ems/06book_sampleComplexity.pdf?dl=0 ch06] | ||
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/x12se5y3heqtyfzo7qx30/04sem.pdf?rlkey=0hd5hphnbj90jc24nqsw63ka7&st=1zie6tp0&dl=0 prob04] ''update 12.10'' |
| − | || | + | || [https://www.dropbox.com/scl/fi/g6j0n39zhm1he8kfena8d/04sol.pdf?rlkey=hcg1cr6s4cca9ekqua67ehlhf&st=81bpsm1a&dl=0 sol04] |
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=8J5B9CCy-ws | + | | [https://www.youtube.com/watch?v=8J5B9CCy-ws 15 Oct] |
|| Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | || 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/rpnh6288rdb3j8m/05slides.pdf?dl=0 sl05] | ||
|| [https://www.dropbox.com/s/eurz2vkvt1wa5zm/07book_growthFunctions.pdf?dl=0 ch07] [https://www.dropbox.com/scl/fi/50oxlmjkx59hjrq82yqvx/08book_VCdimension.pdf?rlkey=5dtlcis378kqu24ttko6s7zpf&dl=0 ch08] | || [https://www.dropbox.com/s/eurz2vkvt1wa5zm/07book_growthFunctions.pdf?dl=0 ch07] [https://www.dropbox.com/scl/fi/50oxlmjkx59hjrq82yqvx/08book_VCdimension.pdf?rlkey=5dtlcis378kqu24ttko6s7zpf&dl=0 ch08] | ||
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/1n9jdc70ia7vu957mls02/05sem.pdf?rlkey=8x89v3fkm1q61b4frirb9nqke&st=7pfvhuq6&dl=0 prob05] |
| − | || <!-- [https://www.dropbox.com/scl/fi/ | + | || <!-- [https://www.dropbox.com/scl/fi/jzm82hqbnzp7931gz8jd2/05sol.pdf?rlkey=o04gco2huwqo4m7rrtp0yd9gl&st=6f0uh0q4&dl=0 sol05] --> |
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=zHau8Br_UFQ | + | | [https://www.youtube.com/watch?v=zHau8Br_UFQ 22 Oct] |
| − | || Risk decomposition and the fundamental theorem of statistical learning theory | + | || Risk decomposition and the fundamental theorem of statistical learning theory (previous [https://www.youtube.com/watch?v=zHau8Br_UFQ recording] covers more) |
|| [https://www.dropbox.com/s/0p8r5wgjy1hlku2/06slides.pdf?dl=0 sl06] | || [https://www.dropbox.com/s/0p8r5wgjy1hlku2/06slides.pdf?dl=0 sl06] | ||
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/th4r5t2gm29en4hejareq/09book_riskBounds.pdf?rlkey=4ox3f26kygxorxft8jlijuf0f&st=fg0fdyx2&dl=0 ch09] |
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/15y2x2pq3pp77144nzee5/06sem.pdf?rlkey=72zoca4wgs472df4izvq2dd3t&st=5m9u4q2u&dl=0 prob06] |
| − | || | + | || [https://www.dropbox.com/scl/fi/w8kc0izfc12sqjyd8hfou/06sol.pdf?rlkey=a09f6yx9e0ifohus9vt2ybthd&st=09qmm3m6&dl=0 sol06] |
|- | |- | ||
| − | | [https:// | + | | [https://youtube.com/live/G5fglRAaXMo 05 Nov] |
|| Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | || Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | ||
|| [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/scl/fi/ohtmf1fwsu9c6vkrj6e5a/10book_measureConcentration.pdf?rlkey=dqsgskp8slui6xoq9c7tx680b&dl=0 ch10] [https://www.dropbox.com/s/hfrvhebbsskbk6g/11book_RademacherComplexity.pdf?dl=0 ch11] | || [https://www.dropbox.com/scl/fi/ohtmf1fwsu9c6vkrj6e5a/10book_measureConcentration.pdf?rlkey=dqsgskp8slui6xoq9c7tx680b&dl=0 ch10] [https://www.dropbox.com/s/hfrvhebbsskbk6g/11book_RademacherComplexity.pdf?dl=0 ch11] | ||
| − | || [https://www.dropbox.com/scl/fi/ | + | || [https://www.dropbox.com/scl/fi/701h3asvj5a6kj7d9p1tm/07sem.pdf?rlkey=dsnhc90gp0nd7jqgy3oicds4i&st=fu4nf10i&dl=0 prob07] |
| − | || | + | || [https://www.dropbox.com/scl/fi/kd3osu95m7bmilv6z6bxm/07sol.pdf?rlkey=9ycz3obscp65uc05pg2dt3zww&st=9d8g3jkf&dl=0 sol07] |
|- | |- | ||
| | | | ||
|| ''Part 3. Margin risk bounds with applications'' | || ''Part 3. Margin risk bounds with applications'' | ||
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=oU2AzubDXeo | + | | [https://www.youtube.com/watch?v=oU2AzubDXeo 12 Nov] |
|| Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | || Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | ||
|| [https://www.dropbox.com/s/oo1qny9busp3axn/08slides.pdf?dl=0 sl08] | || [https://www.dropbox.com/s/oo1qny9busp3axn/08slides.pdf?dl=0 sl08] | ||
|| [https://www.dropbox.com/s/573a2vtjfx8qqo8/12book_regression.pdf?dl=0 ch12] [https://www.dropbox.com/scl/fi/hxeh5btc0bb2f52fnqh5f/13book_SVM.pdf?rlkey=dw3u2rtfstpsb8mi9hnuc8poy&dl=0 ch13] | || [https://www.dropbox.com/s/573a2vtjfx8qqo8/12book_regression.pdf?dl=0 ch12] [https://www.dropbox.com/scl/fi/hxeh5btc0bb2f52fnqh5f/13book_SVM.pdf?rlkey=dw3u2rtfstpsb8mi9hnuc8poy&dl=0 ch13] | ||
| − | || [https://www.dropbox.com/scl/fi/rp2m0dvovdjbvzdl7t1bl/09sem.pdf?rlkey=v1jsm5dagh7tymci5pkqn5gox&dl=0 | + | || [https://www.dropbox.com/scl/fi/rp2m0dvovdjbvzdl7t1bl/09sem.pdf?rlkey=v1jsm5dagh7tymci5pkqn5gox&dl=0 prob08] |
| − | || <!-- [https://www.dropbox.com/scl/fi/e598w1t8tzqxfvn1d4ww1/09sol.pdf?rlkey=yr1gzu8kg2rdkubaelicljj46&dl=0 | + | || <!-- [https://www.dropbox.com/scl/fi/e598w1t8tzqxfvn1d4ww1/09sol.pdf?rlkey=yr1gzu8kg2rdkubaelicljj46&dl=0 sol08] --> |
|- | |- | ||
| − | | [https://youtu.be/9FhFxLHR4eE | + | | [https://youtu.be/9FhFxLHR4eE 19 Nov] |
|| Kernels: RKHS, representer theorem, risk bounds | || Kernels: RKHS, representer theorem, risk bounds | ||
|| [https://www.dropbox.com/s/jst60ww8ev4ypie/09slides.pdf?dl=0 sl09] | || [https://www.dropbox.com/s/jst60ww8ev4ypie/09slides.pdf?dl=0 sl09] | ||
|| [https://www.dropbox.com/scl/fi/lozpqk5nnm8us77qfhn7x/14book_kernels.pdf?rlkey=s8e7a46rm3znkw13ubj3fzzz0&dl=0 ch14] | || [https://www.dropbox.com/scl/fi/lozpqk5nnm8us77qfhn7x/14book_kernels.pdf?rlkey=s8e7a46rm3znkw13ubj3fzzz0&dl=0 ch14] | ||
| − | || [https://www.dropbox.com/scl/fi/9mjmb6deu08ipf38s57bh/10sem.pdf?rlkey=z1khm4i8r39eeqmhargte24s4&dl=0 | + | || [https://www.dropbox.com/scl/fi/9mjmb6deu08ipf38s57bh/10sem.pdf?rlkey=z1khm4i8r39eeqmhargte24s4&dl=0 prob09] |
| − | || <!-- [https://www.dropbox.com/scl/fi/a5c0buap9b1h1ojdbhp3u/10sol.pdf?rlkey=8ft5tjyy1sl5dkj4p4hh8phbc&dl=0 | + | || <!-- [https://www.dropbox.com/scl/fi/a5c0buap9b1h1ojdbhp3u/10sol.pdf?rlkey=8ft5tjyy1sl5dkj4p4hh8phbc&dl=0 sol09] --> |
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=OgiaWrWh_WA | + | | [https://www.youtube.com/watch?v=OgiaWrWh_WA 26 Nov] |
|| AdaBoost and the margin hypothesis | || AdaBoost and the margin hypothesis | ||
|| [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | || [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | ||
|| [https://www.dropbox.com/s/e7m1cs7e8ulibsf/15book_AdaBoost.pdf?dl=0 ch15] | || [https://www.dropbox.com/s/e7m1cs7e8ulibsf/15book_AdaBoost.pdf?dl=0 ch15] | ||
| − | || [https://www.dropbox.com/scl/fi/ykbzx314pdn3mn3jiehli/11sem.pdf?rlkey=hpmtks20a3k5zsvr8jm1iqc35&dl=0 | + | || [https://www.dropbox.com/scl/fi/ykbzx314pdn3mn3jiehli/11sem.pdf?rlkey=hpmtks20a3k5zsvr8jm1iqc35&dl=0 prob10] |
| − | || <!-- [https://www.dropbox.com/scl/fi/c805j4f54ioiozphvh9j0/11sol.pdf?rlkey=6rrxlweaiko1lm0z2ua4k7mqk&dl=0 | + | || <!-- [https://www.dropbox.com/scl/fi/c805j4f54ioiozphvh9j0/11sol.pdf?rlkey=6rrxlweaiko1lm0z2ua4k7mqk&dl=0 sol10] --> |
|- | |- | ||
| − | | [https://youtu.be/GL574ljefJ8 | + | | [https://youtu.be/GL574ljefJ8 03 Dec] |
|| Implicit regularization of stochastic gradient descent in overparameterized neural nets ([https://www.youtube.com/watch?v=ygVHVW3y3wM recording] with many details about the Hessian) | || Implicit regularization of stochastic gradient descent in overparameterized neural nets ([https://www.youtube.com/watch?v=ygVHVW3y3wM recording] with many details about the Hessian) | ||
|| | || | ||
| Строка 111: | Строка 110: | ||
|| | || | ||
|- | |- | ||
| − | | [https://www.youtube.com/watch?v=RDTK7hBqiJY | + | | [https://www.youtube.com/watch?v=RDTK7hBqiJY 10 Dec] |
|| Part 2 of previous lecture: Hessian control and stability of the NTK. | || Part 2 of previous lecture: Hessian control and stability of the NTK. | ||
|| | || | ||
| Строка 151: | Строка 150: | ||
== Office hours == | == Office hours == | ||
| − | Bruno Bauwens: | + | Bruno Bauwens: Bruno Bauwens: Tuesday 12h -- 20h. Wednesday 16h -- 18h. Friday 11h -- 17h. Better send me an email in advance. |
Nikita Lukianenko: Write in Telegram, the time is flexible | Nikita Lukianenko: Write in Telegram, the time is flexible | ||
Текущая версия на 13:45, 5 ноября 2024
Содержание
General Information
Lectures: on Tuesday 9h30--10h50 in room M302 and in zoom by Bruno Bauwens
Seminars: online in Zoom by Nikita Lukianenko.
Please join the telegram group The course is similar to last year.
Homeworks
Deadline every 2 weeks, before the lecture. The tasks are at the end of each problem list. (Problem lists will be updated, check the year.)
Before 3rd lecture, submit HW from problem lists 1 and 2. Before 5th lecture, from lists 3 and 4. Etc.
Classroom to submit homeworks. You may submit in English or Russian, as latex or as pictures. Results are here.
Late policy: 1 homework can be submitted at most 24 late without explanations.
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 21 Sep | Philosophy. The online mistake bound model. The halving and weighted majority algorithms. | sl01 | ch00 ch01 | prob01 | sol01 |
| 24 Sep | The standard optimal algorithm. The perceptron algorithm. | sl02 | ch02 ch03 | prob02 | sol02 |
| 01 Oct | Kernel perceptron algorithm. Prediction with expert advice. Recap probability theory (seminar). | sl03 | ch04 ch05 | prob03 | sol03 |
| Part 2. Distribution independent risk bounds | |||||
| 08 Oct | Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. | sl04 | ch06 | prob04 update 12.10 | sol04 |
| 15 Oct | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | prob05 | |
| 22 Oct | Risk decomposition and the fundamental theorem of statistical learning theory (previous recording covers more) | sl06 | ch09 | prob06 | sol06 |
| 05 Nov | Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | sl07 | ch10 ch11 | prob07 | sol07 |
| Part 3. Margin risk bounds with applications | |||||
| 12 Nov | Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | sl08 | ch12 ch13 | prob08 | |
| 19 Nov | Kernels: RKHS, representer theorem, risk bounds | sl09 | ch14 | prob09 | |
| 26 Nov | AdaBoost and the margin hypothesis | sl10 | ch15 | prob10 | |
| 03 Dec | Implicit regularization of stochastic gradient descent in overparameterized neural nets (recording with many details about the Hessian) | ch16 ch17 | |||
| 10 Dec | Part 2 of previous lecture: Hessian control and stability of the NTK. |
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.
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.
Grading formula
Final grade = 0.35 * [score of homeworks] + 0.35 * [score of colloquium] + 0.3 * [score on the exam] + bonus from quizzes.
All homework questions have the same weight. Each solved extra homework task increases the score of the final exam by 1 point. At the end of the lectures there is a short quiz in which you may earn 0.1 bonus points on the final non-rounded grade.
There is no rounding except for transforming the final grade to the official grade. Arithmetic rounding is used.
Autogrades: if you only need 6/10 on the exam to have the maximal 10/10 for the course, this will be given automatically. This may happen because of extra homework questions and bonuses from quizzes.
Colloquium
Rules and questions from last year.
Date: TBA
Problems exam
TBA
-- You may use handwritten notes, lecture materials from this wiki (either printed or through your PC), Mohri's book
-- You may not search on the internet or interact with other humans (e.g. by phone, forums, etc)
Office hours
Bruno Bauwens: Bruno Bauwens: Tuesday 12h -- 20h. Wednesday 16h -- 18h. Friday 11h -- 17h. Better send me an email in advance.
Nikita Lukianenko: Write in Telegram, the time is flexible
