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
1/21	Week 1: Intro to NLP	Lecture 1	Homework #1: Sentiment Analysis with Logistic Regression Homework #1 quiz will be on 2/4.
1/28	Week 2: Lexical embeddings	Lecture 2	Homework #2: Word2vec Homework #2 quiz will be on 2/11.
2/4	Week 3: Neural networks 101	Lecture 3
2/11	Week 4: Neural networks 201 (neural networks, cont'd; backpropagation)	Lecture 4	Homework #3: Neural Networks from Scratch Homework #3 quiz will be on 2/25.
2/18	Week 5: Neural networks 301 (neural networks, cont'd; activations, initializations, normalization, optimizers)	Lecture 5
2/25	Week 6: Transformer Architecture (Also will finish optimizers discussion)	Lecture 6	Homework #4: Pytorch Implementation Homework #4 quiz will be on 3/4.
3/4	Week 7: Transformer Architecture and Sequence-to-sequence models	Lecture 7	Homework #5: Pre-training Language Model Homework #5 quiz will be on 3/18.
3/11	NO CLASS -- Spring recess	--	--
3/18	Week 8: Sequence-to-sequence models and Machine Translation; Midterm Prep	Lecture 8	Homework #6: Machine Translation Homework #6 quiz will be on 4/1.
3/25	Midterm Exam	no lecture
4/1	Week 9: Transfer learning in NLP; Pre-training variants	Lecture 9	Homework #7, Bert Fine Tuning Homework #7 quiz will be on 4/8.
4/8	Week 10: Model alignment / Preference optimization; PEFT; MoEs	Lecture 10
4/15	Week 11: Positional embeddings, Scaling laws; Distributed training
4/22	Week 12: Paper Presentations
4/29	Week 13: Paper Presentations
5/6	Final Exam