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A tentative plan for the schedule (topics, deadlines, etc.) is available here.

Schedule (subject to change as the term progresses)

Resource
PyTorch Tutorial (w/ links to Colab notebook)
Jan 8
Course Overview
Eisenstein 1
Jan 10
Machine Learning Review - linear classification
Eisenstein 1
Jan 11
Problem Set 0 due
Jan 15
no class - holiday
Jan 17
Machine Learning Review - logistic regression, perceptron, SVM
Eisenstein 2.0-2.5, 4.1, 4.3-4.5, J+M 5
Jan 19
Project 0 due
Jan 22
Machine Learning Review - mutliclass classification
Eisenstein 2.0-2.5, 4.1, 4.3-4.5, J+M 5
Jan 24
Neural Networks - Feedforward, optimization
Eisenstein 2.6, 3.1-3.3, J+M 7, Goldberg 1-4
Jan 29
Problem Set 1 due
Jan 29
Word Embeddings
Eisenstein 3.3.4, 14.5, 14.6, J+M 6, Goldberg 5
Jan 31
Sequence Models - HMM, Viterbi [notes]
Eisenstein 7.0-7.4, J+M 8
Feb 5
Conditional Random Fields [notes]
Eisenstein 7.5, 8.3
Feb 7
Recurrent Neural Networks [notes]
Eisenstein 7.6, Goldberg 10-11, J+M 9.2, 9.3
Feb 8
Project 1 due
Feb 12
no class
Feb 14
Convolutional Neural Networks + Neural CRFs
Eisenstein 3.4, 7.6, Goldberg 9
Feb 19
Encoder-Decoder + MT
Eisenstein 18.3 - 18.5
Feb 21
Attention+ Tokenization
Wu+16 Google NMT, Holtzman+19 Degeneration
Feb 26
Project 2 due
Feb 26
no class
Feb 28
Transformer, Course Project
Vaswani+17 Transformers, Alammar’s blog post, Rush’s tutorial
Mar 4
in-class presentation #1
Mar 6
in-class presentation #2
Mar 11
in-class presentation #3
Mar 12
Problem Set 2 due
Mar 13
Pretrained Language Models (part 1), Midterm Review
ELMo BERT
Mar 25
Pretrained Language Models (part 2) + Ethics
BART, T5
Mar 27
Pretrained Language Models (part 3)
PaLM
Apr 1
Multilingual / Cross-lingual Models
XLM-RoBERTa, Aya
Apr 2
Project 3 due