The lab component of the course will cover mathematical concepts and computational libraries used in computational linguistics. The purpose of the lab is to familiarize you with linear algebra, differential and vector calculus, Numpy, and PyTorch. These concepts are crucial in understanding computational linguistics and natural language processing algorithms covered in lecture.
Lab instructors: Tim Obiso
Place and Time: Friday 2:20 – 3:50 in Volen 119
Lab notes and exercises
Notes and exercises from the lab will be posted here as the semester progresses.
- 1/17: Intro to Numpy and Naive Bayes in Numpy | Numpy tutorial, Numpy tutorial supplement, Naive Bayes in Numpy slides Colab Notebook
- 1/24: Linear Classifiers | slides exercise
- 1/31: Minibatch, Regularization, and Optimizers | slides notebook
- 2/7: Perceptron, Structured Perceptron | slides
- 2/14: Viterbi, Structured Perceptron | slides (same as 2/7) viterbi math visualization (TBA)
- 2/28: Neural Networks 1, Quiz Prep
- 3/7: No recitation
- 3/14: Huggingface NLP Course (Rec: Chapters 2, 5, 6)
- 3/21: Probabilistic CKY | recording (moved to Moodle) slides
- 3/28: Dependency Parsing, PyTorch | slides notebook
- 4/4: RNNs | slides
- 4/11: Transformers and Attention | slides attention video notebook
- 4/18: Brandeis Break No Recitation
- 4/25: Semester Recap | slides photo of board
