Calendar

Rough semester calendar draft; dates/events are subject to change/update.

Module 1 - Intro and Review

Sep/7

Lecture 1 Intro

Sep/7

Homework 0 HW0 out

Homework 0

Sep/8

Recitation 1 Background review

Module 2 - The Basics

Sep/12

Lecture 2 Supervised Learning, ERM

Slides
[SSS] Chapter 1, 2, 3,

Sep/14

Lecture 3 Optimization, regularization

Slides
[SSS] Chapter 12.1.1, 13.1, 13.2

Sep/15

Recitation 2

Sep/19

Lecture 4 Linear vs. nonlinear models, bias variance

[B] Chapter 3, [SSS] Chapter 5, [HTF] Chapter 3

Sep/21

Lecture 5 Intro to PAC Learning

[SSS] Chapter 1, 2, 3,

Sep/22

Holiday student holiday; no recitation

Sep/26

Lecture 6 On-line learning, regret

[SSS] Chapter 21

Sep/28

Lecture 7 Decision problems, bandits

[SB] Chapter 1, 2

Sep/29

Recitation 3

Oct/03

Lecture 8 Neural/deep architectures (supervised)

[HTF] Chapter 11, [JWHT], Chapter 10

Oct/05

Lecture 9 Robustness, stability, adversarial predictions

[KM] tutorial

Oct/06

Recitation 4

Add Date

Oct/09

Holiday indigenous peoples’ day holiday

Oct/10

Holiday student holiday; no lecture

Oct/12

Lecture 10 Uncertainty, conformal prediction

[AB] tutorial

Oct/13

Recitation 5

Oct/17

Lecture 11 Complexity, generalization

[SSS] Chapter 6, [HTF] Chapter 7.9

Oct/19

Exam 1 Walker

Oct/20

Recitation 6

Module 3 - Deep Dive

Oct/24

Lecture 12 Unsupervised learning, dimensionality reduction

[HTF] Chapter 14.5, [JWHT], Chapter 12.1-12.2

Oct/26

Lecture 13 Generative models, auto-regressive

[B] Chapter 9, [HTF] Chapter 14.3.7

Oct/27

Recitation

Oct/31

Lecture 14 Deep generative models, VAEs

[KW]

Nov/02

Lecture 15 Diffusion models

[SWMG],[HJA],[SE]

Nov/03

Recitation

Nov/07

Lecture Markov Decision Processes

Slides
[SB] Chapter 3

Nov/09

Lecture Policy-based RL

Slides
[SB] Chapter 6, 13

Nov/10

Holiday veterans day holiday; no recitation

Nov/14

Lecture Deep RL (e.g., DQN and TROP/PPO)

[TRPO], [PPO], [DQN]

Nov/16

Lecture Covariate shift, domain adaptation

[SKM], [GUAG], [CFTR],[Gul], [PC]

Nov/17

Recitation

Nov/21

Lecture Few-shot learning, in-context learning

Nov/22

Drop Date

Nov/23

Holiday thanksgiving holiday; no lecture

Nov/24

Holiday thanksgiving holiday; no recitation

Nov/28

Lecture Self-supervised learning, masking, contrastive

Nov/30

Lecture Foundation models

Dec/01

Recitation

Dec/05

Lecture State-of-the-art LLMs

Dec/07

Exam 2 Walker

Dec/08

Recitation Project help

Dec/12

Lecture Some Recent trends/ applications

Slides

Recommended Reading

All freely accessible (an MIT IP may be required):

• [B] Pattern Recognition and Machine Learning, Bishop; Springer, 2006.
• [HTF] The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman; Springer, 2009.
• [SB]/[SSS] Understanding Machine Learning: From Theory to Algorithms, Shalev-Shwartz and Ben-David; Cambridge University Press, 2014.
• [SB] Reinforcement Learning: An Introduction, Sutton and Barton; The MIT Press, 2018.
• [JWHT] An Introduction to Statistical Learning, James, Witten, Hastie, and Tibshirani; Springer, 2013.