DSC 291 Machine Learning with Few Labels

Date	Lecture	Suggested Readings	Logistics
04/01	Lecture #1: Overview [ slides ] [ slides ]
04/03	Lecture #2: Overview [ slides/marked: see above ]
04/05	Lecture #3: Overview [ slides ] [ marked ]	Hu and Shu, 2023: Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning
04/08	Lecture #4: Large Language Model Basics, Self-Supervised Learning [ slides ] [ marked ]	Lecun and Misra, (Blog) Self-supervised learning: The dark matter of intelligence
04/10	Lecture #5: Large Language Model Basics, Self-Supervised Learning [ slides ] [ marked ]	J Eisenstein, Chapter 6 (Language Models)
04/12	Lecture #6: Enhancing Large Language Models [ slides ]	Rich Sutton, The Bitter Lesson
04/15	Lecture #7: Enhancing Large Language Models [ slides ] [ marked ]	Nature article, Lightning talks: science in 5 minutes or less
04/17	Lecture #8: Enhancing Large Language Models [ slides/marked: see above ]
04/19	Lecture #9: Enhancing Large Language Models [ slides ]		Project proposal due
04/22	Lecture #10: Enhancing Large Language Models [ slides ]	Brownlee, No Free Lunch Theorem for Machine Learning
04/24	Lecture #11: Supervised, Unsupervised Learning [ slides ]	PRML, Chapter 1 (Basic introduction of some ML concepts)
04/26	Lecture #12: Unsupervised Learning: EM Algorithm [ slides ] [ marked ]	PRML, Chapter 9 (Mixture Models and EM)
04/29	Lecture #13: Unsupervised Learning: EM Algorithm [ slides ] [ marked ]
05/01	(No class)
05/03	Lecture #14: Unsupervised Learning: EM, Variational Inference [ slides ] [ marked ] [ recording ]	PRML, Chapter 10.1-10.3 (Variational Inference)
05/06	Lecture #15: Guest Lecture: LM, Grounding, Embodied AI [ slides ] [ recording ]
05/08	Lecture #16: Variational Inference [ slides ] [ marked ] [ recording ]	PRML, Chapter 10.1-10.3 (Variational Inference)
05/10	(No class)
05/13	Lecture #17: Variational Inference [ slides ] [ marked ]
05/15	Lecture #18: Variational Inference [ slides ] [ marked ]		Project mid-term report due
05/17	Lecture #19: Variational Inference [ slides ] [ marked ]	Deep Learning book, Chap 20.10.3 (Variational Autoencoder) (Optional) Kingma and Welling, 2014, Variational Autoencoders
05/20	Lecture #20: Variational Inference; Reinforcement Learning [ slides ] [ marked ]
05/22	Lecture #21: Reinforcement Learning [ slides ] [ marked ]	Lilian Weng, A (Long) Peek into Reinforcement Learning
05/24	Lecture #22: Reinforcement Learning [ slides ] [ marked ]	Sutton, Barto, Reinforcement Learning, An Introduction, Chapter 13.
05/27	Memorial Day observance (No class)
05/29	Lecture #23: Reinforcement Learning [ slides ] [ marked ]
05/31	Lecture #24: "Standard Model" of ML [ slides ]	(Optional) Hu and Xing, Toward A "Standard Model" of Machine Learning
06/03	Lecture #27:
06/05	Lecture #28: Final project presentation
06/07	Lecture #29: Final project presentation
Final Week			Project final report due

04/01

Lecture #1:
Overview
[ slides ] [ slides ]

04/03

Lecture #2:
Overview
[ slides/marked: see above ]

04/05

Lecture #3:
Overview
[ slides ] [ marked ]