Statistical learning theory 2021 — различия между версиями
|Строка 59:||Строка 59:|
|| ''Part 2.
|| ''Part 2. classification'' || || ||
| 25 Sept
| 25 Sept
|| Sample complexity in the realizable setting, simple
|| Sample complexity in the realizable setting, simple and bounds using VC-dimension
Версия 00:39, 25 сентября 2021
Lectures: Saturday 14:40 - 16:00. The lectures are Pokrovkaya and also streamed here in zoom. On 18 and 25 Sept only online.
Seminars: Tuesday 16:20 - 17:40. The seminars are Pokrovkaya and also streamed here in zoom.
See ruz for the rooms.
Practical information on telegram group
The course is similar last year, except for the order of topics and part 3.
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW.
Deadline before the lecture, every 2 weeks.
25 Sept: see problem lists 1 and 2 [update Sept 16], [Hint for 2.8 added on Sept 23.]
09 Oct: see problem lists 3 and 4
|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||01sl||00ch 01ch||01prob (9 Sept)||01sol|
|11 Sept||The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm.||02sl||02ch 03ch||02prob (23 Sept)||02sol|
|18 Sept (online)||Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory.||03sl||ch05 ch04 todo||03prob||03sol|
|Part 2. Risk bounds for binary classification|
|25 Sept||Sample complexity in the realizable setting, simple examples and bounds using VC-dimension||ch06|
|2 Oct||Risk decomposition and the fundamental theorem of statistical learning theory|
|9 Oct||Rademacher complexity|
|16 Oct||Support vector machines and margin risk bounds|
|29 Oct||Kernels: risk bounds, design, and representer theorem|
|6 Nov||AdaBoost and risk bounds|
|Part 3. Other topics|
|20 Nov||Dimensionality reduction and the Johnson-Lindenstrauss lemma|
|27 Nov||Active learning|
|4 Dec||Extra space for a lesson, in the likely case we are a bit slower.|
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/ .
|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.