Statistical learning theory
Материал из Wiki - Факультет компьютерных наук
Версия от 15:43, 23 сентября 2017; Bbauwens (обсуждение | вклад)
General Information
Course materials
Date | Summary | Lecture notes | Problem list |
---|---|---|---|
5 sept | PAC-learning and VC-dimension: definitions | 1st and 2nd lecture Updated on 13th of Sept. | Problem list 1 |
12 sept | PAC-learning and VC-dimension: proof of fundamental theorem | Problem list 2 | |
19 sept | Sauer's lemma, neural networks and agnostic PAC-learning | 3th lecture Updated on the 23th of Sept. | Problem list 3 |
26 sept | Agnostic PAC-learning and Computational learning theory | ||
3 okt | Boosting: the adaBoost algorithm | ||
10 okt | Boosting: several other algorithms | ||
17 okt | Online learning algorithms |
|
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.
Office hours
Person | Monday | Tuesday | Wednesday | Thursday | Friday | ||
---|---|---|---|---|---|---|---|
|
Bruno Bauwens | 15:05–18:00 | 15:05–18:00 | Room 620 | |||
|
Quentin Paris |