Class schedule

This schedule is tentative and subject to change. Please check back here often.

Setting up the environment
Note: We do not need this for HW1 and 2, but please go through it and set up the environment on your system, as we will need it for later HWs.

Lecture slides and handouts will be available in this folder. Additional reading and video materials will be posted on the class discord.

N/A

Date	Lecture topic	Slides / Handouts	Homework
1/17	Week 1: Intro to NLP	Lecture 1	Homework #1: Sentiment analysis with logistic regression. Homework #1 is due 1/23, 11:59 p.m. EST.
1/24	Week 2: Lexical embeddings	Lecture 2	Homework #2: Word2vec. Homework #2 is due 1/30, 11:59 p.m. EST.
1/31	Week 3: Neural networks 101	Lecture 3: [Slides] [Models, Losses, Optimizers] [Entropy, MI, KL-divergence]	Homework #3 is due 2/13 11:59 p.m. EST.
2/7	Week 4: Neural networks 201	Lecture 4: [Slides] [Backprop] [Animated]
2/14	Week 5: Neural networks 301 (activations, w initializations, normalization and optimizers)	Lecture 5: [Slides]	Homework #4 is due 2/20 11:59 p.m. EST.
2/21	Week 6: Attention mechanism	Lecture 6: [Slides]	Homework #5 Part1 is due 2/27 11:59 pm and Part 2 is due 3/12 11:59 p.m. EST.
2/28	Week 7: Sequence-to-sequence models	Lecture 7 [Slides]
3/6	No class -- Spring recess
3/13	Week 8: Transfer learning in NLP	Lecture 8 [Transfer Learning Slides]
3/20	Week 9: Midterm Interview	no lecture	Homework #6 Part1 is due 3/26 11:59 pm and Part 2 is due 4/2 11:59 p.m. EST.
3/27	Week 10: Pre-training variants	Lecture 10 [Pretraining variants Slides]
4/3	Week 11: Effects of scale in NLP	Lecture 11 [Slides]
4/10	Week 12: Long Context; Alignment	Lecture 12 [Long Context Slides] [Alignment Slides] [Presentation guidelines]
4/17	Week 13: Paper Presentations	no lecture
4/24: MOE Models	Week 14	Lecture 14 [MOE Slides] [Summary of MOE Papers]