Lectures

Lecturer Varun Kanade
Hours Monday 16h-18h and Wednesday 14h-15h
Location Lecture Theatre A (LTA)

Note 1. Lecture notes will be posted for every lecture; however, they are not meant to be used as a substitute for actually reading the textbooks and other posted readings.

Note 2. There will be parts of suggested reading material that you struggle with or may seem not directly related to what we covered in the lecture. I suggest you skip these sections and then only return to them once you've understood the material covered in the lecture and want to increase your understanding further.


Lecture Schedule

Date Topic Handouts Reading
16/1/2017 Introduction & PAC Learning Framework [slides]
[notes] 		(KV) Chap 1
Valiant's paper
18/1/2017 Learning Conjunctions, Intractability of Learning 3-term DNF [notes] (KV) Chap 1
23/1/2017 Learning 3-CNF, Consistent Learners, Occam's Razor [notes] (KV) Chap 2
25/1/2017 VC-Dimension, Sample Complexity Lower Bounds [notes] (KV) Chap 3
30/1/2017 Growth Function, Sample Complexity Upper Bounds [notes] (KV) Chap 3
01/2/2017 Weak Learning and Boosting [notes] Adaboost survey
Adaboost article
06/2/2017 Cryptographic Hardness of Learning; Exact Learning using MQ + EQ [notes]
[notes]

 (KV) Chap. 6, Chap. 8.1, 8.2
08/2/2017 Learning DFA using Queries [James Worrell's notes] (KV) Chap. 8.3
13/2/2017 Learning in the presence of noise; SQ model [notes] (KV) Chap. 5
[Kearns' paper]
15/2/2017 Learning Real-valued Functions,
Convex Optimisation		 [notes] Convex Opt. book (Chap. 3.1)
20/2/2017 Generalised Linear Models,
Rademacher Complexity		 [notes] (MRT) Chap. 3.1, 4.4, 10
Learning GLMs paper
22/2/2017 Agnostic Learning [notes] Agnostic Learning article
27/2/2017 Agnostically Learning Halfspaces [notes] Agnostically Learning Halfspaces article
01/3/2017 Online Learning; Perceptron [Francisco's notes] Littlestone's paper