Lectures

Lecturer Varun Kanade
Hours 3-4:30pm Monday, Tuesday, Thursday (Weeks 5-8 Hilary Term, Weeks 1-3 Trinity Term)
Location Zoom link distributed by email
Notes (Draft Lecture Notes)

Note 1. A set of lecture notes (LN) covering the entire course will be posted. These are not meant to be used as a substitute for reading the recommended textbooks and other posted reading. There might be some delay in updating the lecture notes. Should you wish to read up ahead of lectures, you may follow the material posted on the Computational Learning Theory course from Michaelmas Term 2018. Please make sure to make use of the most updated material when revising.

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 only return to them once you've understood the material covered in the lecture and want to increase your understanding further.

Note 3. Some Monday and Tuesday lectures may be slightly longer than one hour.


Lecture Schedule

Date Topic Whiteboard Reading

15/02/2021 PAC Learning Framework [board] Chapter 1.1 (LN)
Chapter 1.1 (KV)

16/02/2021 Learning Conjunctions [board] Chapter 1.2 (LN)
Chapter 1.2 (KV)

18/02/2021 Intractability of Learning 3-term DNF; PAC Learning [board] Chapter 1.3-8 (LN)
Chapter 1.3-6 (KV)
Valiant's paper

22/02/2021 Occam's Razor [board] Chapter 2 (LN)
Chapter 2 (KV)

23/02/2021 VC Dimension, Sauer's Lemma [board] Chapter 3 (LN)
Chapter 3 (KV)

25/02/2021 VC Dimension: Sample Complexity Bounds [board] Chapter 3 (LN)
Chapter 3 (KV)

01/03/2021 VC Dimension: Sample Complexity Bounds
Boosting
[board] Chapter 4 (LN)
Adaboost survey
Adaboost article

02/03/2021 Boosting;
Cryptographic Hardness of Learning
[board] Chapter 5 (LN)
Chapter 6 (KV)

04/03/2021 Cryptographic Hardness of Learning;
Learning using Membership & Equivalence Queries

[board] Chapter 6 (LN)
Chapter 8 (KV)

08/03/2021 Learning DFAs using MQs + EQs [board] Chapter 6 (LN)
Chapter 8 (KV)

09/03/2021 Learning in the presence of Random Classification Noise (RCN)
Statistical Query (SQ) Model

[board] Chapter 7 (LN)
Chapter 5 (KV)

11/03/2021 Statistical Query (SQ) Learning

[board] Chapter 7 (LN)
Chapter 5 (KV)

26/04/2021 Learning Real-Valued Functions
Convex Optimization

[board] Chapter 8 (LN)
Chapter 10 (MRT)
Convex Opt. book (Chap. 3.1)

27/04/2021 Rademacher Complexity

[board] Chapter 3 (MRT)
Chapter 10 (MRT)

29/04/2021 Mistake-bounded Learning

[board] [notes]
Littlestone's paper

03/05/2021 Perceptron and Winnow

[board] [notes]
Littlestone's paper

04/05/2021 Online Learning with Expert Advice [board] MWUA Survey

06/05/2021 Online Learning with Expert Advice [board] MWUA Survey