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 |