Schedule (Tentative)

Week

Topic

Assignment

Readings

Week 1: 9/5 & 9/8

What is NLP?

Processing Text (remote class)

Homework 0 (due 9/7)

HW 0 Data

Homework 1 (due 9/14)

Starter Code

Bisk et al. (2021)

Reference: Think Bayes Chapter 2

Week 2: 9/12 & 9/15

Language and Probability

Tokenization

Zipfian distribution program

Homework 2 (due 9/21)

Starter Code

J&M 2

Week 3: 9/19 & 9/22

Language Models

LM Evaluation and Smoothing video

N-gram live coding video

N-gram live coding file

N-gram live coding data

N-gram live coding PDF description

Homework 3 (due 9/28)

Starter Code

J&M 3

Week 4: 9/26 & 9/29

Naive Bayes Classifiers

Evaluating Classification

Homework 4 (due 10/5)

Starter Code

J&M 4

Reference: Naive Bayes recap

Week 5: 10/3 & 10/6

Vector Semantics

Word2Vec

J&M 6

Week 6: 10/13

Regression

J&M 5

Reference: Regression in more depth

Week 7: 10/17 & 10/20

Gradient Descent

Regression program

Goodreads titles

Goodread language ids

Midterm 1 (10/20)

Reference documentation

Reference equations

Homework 5 (due 10/26)

Starter Code

J&M 7

Week 8: 10/24 & 10/27

Neural networks

Feedforward networks

Feedforward classifier program

Homework 6 (due 11/9)

Starter Code

J&M 7

Week 9: 10/31 & 11/3

Neural language models

RNNs

J&M 9

Reference: Neural MT blog post

Week 10: 11/7 & 11/10

Attention

Transformers

J&M 10

Week 11: 11/17

Midterm 2 (11/17)

Homework 7 (due 11/27)

Starter Code

Reference: GPT-2 blog post

Stochastic Parrots

Week 12: 11/20

Whiteboard practice

Final Project

J&M 13-14

Week 13: 11/28 & 12/1

Guest lecture: Alex Warstadt

Transfer Learning

Homework 8 (due 12/6)

Starter Code

J&M 11

BERT paper

Overview of transfer learning

Week 14: 12/5 & 12/8

Guest lecture: Francesca Lucchetti

Future of NLP

Sproat (2022)

Blasi et al. (2021)

Week 15: 12/12

Project presentations

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