Schedule

Subject to change.

Lecture No	Date	Topics	Readings	Lecture Slides
0	Mon Aug 28	Introduction		Course Introduction
1	Wed Aug 30	Math Review	Linear Algebra Review I Linear Algebra Review II Matrix Cookbook	Math Review
2	Wed Sept 6	Concentration Bounds	Concentration Bounds	Concentration Bounds I
3	Mon Sept 11	Concentration Bounds	Basic Tail and Concentration bounds	Concentration Bounds II Concentration Bounds III
4	Wed Sept 13	Concentration Bounds		Concentration Bounds III
5	Mon Sept 18	PAC Learning	Measure Theory	PAC Learning I
6	Wed Sept 20	PAC Learning	Foundations of Machine Learning Chapter 2 & 3	PAC Learning II
7	Mon Sept 25	PAC Learning		PAC Learning III
8	Wed Sept 27th	PAC Learning		PAC Learning IV
9	Mon Oct 2nd	Rademacher Complexity	Foundations of Machine Learning Chapter 3	Rademacher Complexity
10	Wed Oct 4th	VC Dimension	Foundations of Machine Learning Chapter 3	VC Dimension
11	Mon Oct 9th	Boosting		Boosting I
12	Wed Oct 11th	Boosting		Boosting II
13	Wed Oct 16th	Reinforcement Learning	Algorithms for Reinforcement Learning	Reinforcement Learning I
14	Mon Oct 30th	Reinforcement Learning	Algorithms for Reinforcement Learning	Reinforcement Learning II
15	Mon Nov 6th	Reinforcement Learning		Reinforcement Learning III
16	Wed Nov 8th	Reinforcement Learning		Reinforcement Learning IV
17	Mon Nov 13th	Reinforcement Learning		Reinforcement Learning V
19	Mon Nov 20th	Reinforcement Learning		Reinforcement Learning VI
20	Mon Nov 27th	LLM	UC Berkeley CS294-158, Alec Radford, OpenAI	LLM 1
21	Wed Nov 29th	LLM	UC Berkeley CS294-158, Alec Radford, OpenAIs	LLM 2
22	Mon Dec 4th	RLHF		RLHF

Algorithms in Machine Learning: Guarantees and Analyses