Statistical learning theory 2024/25 — различия между версиями

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Lectures: on TBA in room TBA and in [https://us02web.zoom.us/j/82300259484?pwd=NWxXekxBeE5yMm9UTmwvLzNNNGlnUT09 zoom] by [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens]
 
Lectures: on TBA in room TBA and in [https://us02web.zoom.us/j/82300259484?pwd=NWxXekxBeE5yMm9UTmwvLzNNNGlnUT09 zoom] by [https://www.hse.ru/en/org/persons/160550073 Bruno Bauwens]
  
Seminars: on TBA in room TBA and in TBA by [https://www.hse.ru/org/persons/225553845/ Nikita Lukianenko].
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First lecture Saturday 21.09 at 10h00 in room R208 (and on the above zoom link).  
  
To discuss the materials and practical issues, 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].
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Seminars: online by [https://www.hse.ru/org/persons/225553845/ Nikita Lukianenko].
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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 ==

Версия 15:32, 20 сентября 2024

General Information

Lectures: on TBA in room TBA and in zoom by Bruno Bauwens

First lecture Saturday 21.09 at 10h00 in room R208 (and on the above zoom link).

Seminars: online 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: see problem lists 1 and 2. Before 5th lecture: see problems lists 3 and 4. Etc.

Email homeworks to brbauwens-at-gmail.com. Start the subject line with SLT-HW. Results will be 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
?? Sept Philosophy. The online mistake bound model. The halving and weighted majority algorithms. sl01 ch00 ch01 prob01
?? Sept The perceptron algorithm. Kernels. The standard optimal algorithm. sl02 ch02 ch03 prob02
?? Sept Prediction with expert advice. Recap probability theory (seminar). sl03 ch04 ch05 prob03
Part 2. Distribution independent risk bounds
?? Oct Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. sl04 ch06 prob05
?? Oct Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions sl05 ch07 ch08 prob06
?? Oct Risk decomposition and the fundamental theorem of statistical learning theory sl06 ch09 prob07
?? Oct Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. sl07 ch10 ch11 prob08
Part 3. Margin risk bounds with applications
?? Nov Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization sl08 ch12 ch13 prob09
?? Nov Kernels: RKHS, representer theorem, risk bounds sl09 ch14 prob10
?? Nov AdaBoost and the margin hypothesis sl10 ch15 prob11
?? Nov Implicit regularization of stochastic gradient descent in overparameterized neural nets (recording with many details about the Hessian) ch16 ch17
?? 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: TBA

Nikita Lukianenko: Write in Telegram, the time is flexible