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.

Date	Lecture topic	Slides / Handouts	Homework
1/27	Week 1: Intro to NLP	Lecture 1	Homework #1: Sentiment Analysis with Logistic Regression Homework #1 is due 2/3, 5:59 p.m. EST.
2/3	Week 2: Lexical embeddings	Lecture 2	Homework #2: Word2vec Homework #2 is due 2/10, 5:59 p.m. EST.
2/10	Week 3: Neural networks 101	Lecture 3	Homework #3: Neural Networks from Scratch Homework #3 is due 2/24, 5:59 p.m. EST.
2/17	NO CLASS - President's Day
2/24	Week 5: Neural networks 301 (backprop, activations, w initializations, normalization and optimizers)	Lecture 4	Homework #4: Pytorch Implementation Homework #4 is due 3/3, 5:59 p.m. EST.
3/3	Week 6: Transformer Architecture	Lecture 5	Homework #5: Pre-training Language Model Homework #5 is due 3/17, 5:59 p.m. EST.
3/10	NO CLASS -- Spring recess	--	--
3/17	Week 7: Sequence-to-sequence models	Lecture 6	Homework #6: Machine Translation Homework #6 is due 4/07, 5:59 p.m. EST. Quiz Solutions
3/24	Week 8: Transfer learning in NLP	Lecture 7
3/31	Week 9: Midterm Exam	no lecture
4/7	Week 10: Pre-training variants; Model alignment / Preference optimization	Lecture 8
4/14	Week 11: Position Emb. and LLM Inference	Lecture 9	Homework #7: BERT Fine Tuning Homework #7 is due 4/21, 5:59 p.m. EST.
4/21	NO CLASS (Patriot's Day)
4/25 (Monday schedule on Friday)	Week 12: Long Context
4/28	Week 13: Paper Presentations
5/5	Week 14: Final Exam