Statistical learning theory 2023/24 — различия между версиями

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Версия 09:59, 22 сентября 2023

General Information

Lectures: Tuesday 14h40 -- 16h00, room S321 and in zoom by Bruno Bauwens and Maxim Kaledin,

Seminars: Monday 16h20 -- 17h40, room N506, and in zoom by Artur Goldman.

To discuss the materials, join the telegram group The course is similar to last year.


Homeworks

Deadline every 2 weeks, before the seminar at 16h00. Homework problems from

seminars 1 and 2 on September 25, seminars 3 and 4 on October 9, seminars 5 and 6 on October 23, seminars 7 and 8 on November 13, seminars 9 and 10 on November 27, seminars 11 and 12 before the start of the exam.

Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. -Link with results todo-.


Course materials

Video Summary Slides Lecture notes Problem list Solutions
Part 1. Online learning
05 Sept Philosophy. The online mistake bound model. The halving and weighted majority algorithms


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.

There is no rounding except on the final grade. Grades fractional grades above 5/10 are rounded up, those below 5/10 are rounded down.

Autogrades: if you only need 4/10 to pass with maximal final score, it will be given automatically. This may happen because of extra questions and bonuses from quizzes.


Colloquium

Rules and questions of previous year.


Problems exam

December 21--30, 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: Wednesday 13h-16h, Friday 14h-20h, (better send an email in advance).

Maxim Kaledin: Write in Telegram, the time is flexible


Источник — «http://wiki.cs.hse.ru/index.php?title=Statistical_learning_theory_2023/24&oldid=80061»