Statistical learning theory 2020 — различия между версиями

Материал из Wiki - Факультет компьютерных наук
Перейти к: навигация, поиск
Строка 57: Строка 57:
 
|| [https://www.dropbox.com/s/nhxkxfjajzsgfnf/04sol.pdf?dl=0 Solutions 4]
 
|| [https://www.dropbox.com/s/nhxkxfjajzsgfnf/04sol.pdf?dl=0 Solutions 4]
 
|-
 
|-
| 10 Oct || PSupport vector machines and risk bounds
+
| 10 Oct || Support vector machines and risk bounds
 
|| Chapt 5, Mohri et al, see below  
 
|| Chapt 5, Mohri et al, see below  
 
||  
 
||  

Версия 23:56, 9 октября 2020

General Information

Teachers: Bruno Bauwens and Vladimir Podolskii

Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901

Seminars
- Group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom https://zoom.us/j/94186131884,
- Group 2: Tuesday 18h, Nikita Lukyanenko, see ruz.hse.ru

Practical information on telegram group

Homeworks: deadlines every 2 weeks, before the lecture.
- Sat 3 Oct: see problem lists 1 and 2
- Sat 17 Oct: see problem lists 3 and 4
- Sat 31 Oct: see problem lists 5 and 6
- Sat 14 Nov: see problem lists 6 and 7
- Sat 28 Nov: see problem lists 8 and 9

Results.

Email homeworks to Brbauwens <at> gmail.com. Start the subject line with SLT-HW. You may submit handwritten solutions.

Course materials

Date Summary Lecture notes Slides Video Problem list Solutions
12 Sept Introduction and sample complexity in the realizable setting lecture1.pdf slides1.pdf Problem list 1 Update 26.09, prob 1.7 Solutions 1
19 Sept VC-dimension and sample complexity lecture2.pdf slides2.pdf Chapt 2,3 Problem list 2 Solutions 2
26 Sept Risk bounds and the fundamental theorem of statistical learning theory lecture3.pdf slides3.pdf Problem list 3 Solutions 3
03 Oct Rademacher complexity lecture4.pdf slides4.pdf Problem list 4 Update 09.10, prob 4.3,4.6 Solutions 4
10 Oct Support vector machines and risk bounds Chapt 5, Mohri et al, see below Problem list 5
17 Oct Support vector machines and kernels.
31 Oct Adaboost
07 Nov Online learning 1
14 Nov Online learning 2
21 Nov Active learning
28 Nov Unsupervised learning
5 Dec Optional: Neural networks and stochastic gradient descent
12 Dec Optional: Neural networks and stochastic gradient descent


The lectures in October are based on the book: Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. These books can be downloaded from http://gen.lib.rus.ec/ .

In November, we follow the lecture notes by Н. К. Животовский

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 14h-18h 16h15-20h Room S834 Pokrovkaya 11

It is always good to send an email in advance. Questions are welcome.