Lecture Notes

Tuesday 09-03: Intro, Text as Strings, Classification, Sentiment Analysis, Features, Weights

Tuesday 09-05: Matrix Representations, Weights and Bias, Sigmoid, Model Predictions for Binary Classification

Tuesday 09-17: Bag of Words Representation

Thursday 09-19: TFIDF features for documents

Tuesday 09-24: Word-level feature representations and vector semantics

Tuesday 10-01: Multi-class classification

Thursday 10-03: Cross-entropy loss and gradient descent

Thursday 10-10: Notebook 00: Python basics

Thursday 10-17: Notebook 01: Python data structures

Thursday 10-24: Notebook 02: PyTorch and a toy model

Tuesday 10-29: Notebook 03: Text data with Pandas and Sklearn

Tuesday 11-05: Notebook 04: Training and testing

Thursday 11-07: Notebook 05: Multilayer perceptron models for classification

Tuesday 11-12: Intro to n-gram language models (see Chapter 3 of Speech and Language Procesing)