Schedule (Tentative)

Week

Topic

Assignment

Readings

Week 1: 9/2 & 9/5

What is NLP?

Processing Text

Homework 0 (due 9/4)

Starter Code

Homework 1 (due 9/11)

Starter Code

Bisk et al. (2021)

Reference: Think Bayes Chapter 2

Week 2: 9/9 & 9/12

Processing Text

Tokenization

Homework 2 (due 9/18)

Starter Code

J&M 2

Week 3: 9/16 & 9/19

Language Models

LM Evaluation and Smoothing

Homework 3 (due 9/25)

Starter Code

J&M 3

Week 4: 9/23 & 9/26

Naive Bayes Classifiers

Evaluating Classification

Homework 4 (due 10/2)

Starter Code

J&M Appendix B

Reference: Naive Bayes recap

Week 5: 9/30 & 10/3

Vector Semantics

Word2Vec

J&M 5

Week 6: 10/7 & 10/10

Regression

Midterm 1 (10/10)

J&M 4

Reference: Regression in more depth

Week 7: 10/17

Gradient Descent

Homework 5 (due 10/23)

Starter Code

Week 8: 10/21 & 10/24

Neural networks

Feedforward networks

Homework 6 (due 10/30)

Starter Code

J&M 6

Week 9: 10/31

RNNs

J&M 7

Reference: Neural MT blog post

Week 10: 11/4 & 11/7

Attention

Midterm 2 (11/17)

J&M 8

Week 11: 11/11 & 11/14

Transformers

Homework 7 (due 11/17)

Starter Code

J&M 10

Reference: GPT-2 blog post

Reference: BERT paper

Week 12: 11/18 & 11/21

Transfer Learning and Alignment

Prompt Engineering and Alignment

Homework 8 (due 11/24)

Starter Code

J&M 9

Reference: Overview of transfer learning

Week 13: 11/25

Whiteboard practice

Final Project

Week 14: 12/2 & 12/5

Interpretability and Future of NLP

Guest lecture: Jin Zhao

Blasi et al. (2021)

Week 15: 12/9

Project presentations