DSC 291 Advanced Statistical Natural Language Processing

Date	Lecture	Readings	Logistics
03/29	Lecture #1: Overview [ slides ]
03/31	Lecture #2 Machine Learning Basics [ slides (updated) ]	M Jordan, The exponential family: Basics (Optional) PRML, Appendix D. Calculus of Variations
04/05	Lecture #3 Language Modeling [ slides (updated) ]	J Eisenstein, Chapter 6 (Language Models)
04/07	Lecture #4 Language Modeling, Representation Learning [ slides (updated) ]	Lecun and Misra, (Blog) Self-supervised learning: The dark matter of intelligence
04/12	Lecture #5 Representation Learning [ slides ]	Weng, (Blog) Contrastive Representation Learning (Optional) Ting et al. A Simple Framework for Contrastive Learning of Visual Representations
04/14	Lecture #6 Representation Learning [ slides (updated) ]	Borman, The EM algorithm (A short tutorial) (Optional) PRML, Chapter 9 (Mixture Models and EM)	HW1 out 04/14. Project proposal due 04/17
04/19	Lecture #7 Representation Learning [ slides (updated) ]
04/21	Lecture #8 Representation Learning [ slides (updated) ]	PRML, Chapter 10.1-10.3 (Variational Inference) (Optional) Bowman et al., Generating Sentences from a Continuous Space
04/26	Lecture #9 Classification [ slides (updated) ]	(Optional) Liu et al., Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
04/28	Lecture #10 Sequence Tagging [ slides ]	J Eisenstein, Chapter 7 (Sequence labeling)
05/03	Lecture #11 Sequence Tagging, Parsing [ slides ]	J Eisenstein, Chapter 10 (Context-free parsing), 10.1 - 10.4	HW1 due, HW2 out
05/05	Lecture #12 Parsing, Text Generation [ slides ]	J Eisenstein, Chapter 18 (Machine Translation)
05/10	Lecture #13 Text Generation [ slides ]	Ranzato et al., Sequence Level Training with Recurrent Neural Networks (Optional) Sutton, Barto, Reinforcement Learning, An Introduction, Chapter 13.
05/12	Lecture #14 Text Generation [ slides ]	N/A
05/17	Lecture #15 Text Generation [ slides ]	Hu et al., Toward Controlled Generation of Text
05/19	Lecture #16 Text Generation [ slides ]	N/A	HW2 due Project mid-term report due
05/24	Lecture #17 Advanced Topics [ slides ]	Goodfellow et al., Generative Adversarial Nets
05/26	Lecture #18 Advanced Topics [ slides ]	Yu et al., SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
05/31	Lecture #19 Advanced Topics [ slides ]	N/A
06/02	Final Project Presentation
Final Week			Project final report due

03/29

Lecture #1:
Overview
[ slides ]

03/31

Lecture #2
Machine Learning Basics
[ slides (updated) ]