NLP class

Lesson	Topic	Details
1	Intro 1	History of NLP, overview of course, class admin
2	Intro 2	pandas, IO, spacy, POS, parses, stopwords
3	Representations	Discrete and sparse representations
4	Embeddings	Word2vec, doc2vec
5	Information retrieval 1	TF-IDF, Entropy, and PMI
6	Information retrieval 2	Collocations and RegEx
7	Language models 1	probabilistic models, architecture, goals
8	Language models 2	Trigram MLE model, smoothing
9	Topic models 1	Model architecture, priors
10	Topic models 2	Seeded topic models, preprocessing
11	Dimensionality reduction and Clustering	PCA/SVD, NMF, k-means, agglomerative clustering
12	Visualization	t-SNE, RGB mappings, seaborn
13	Midterm Project practice
14	Retrofitting
15	Text classification	Intro to classification, ethics
16	Improving classification performance and insights	metrics, significance, model and feature selection, regularization, RLR
17	Application: Sentiment Analysis	SA with LR and RLR
18	Neural networks basics	History, architecture, activation function, loss function, input-output differences
19	NN2	Feed-forward Multilayer Perceptron in keras
20	NN3	Convolutional neural networks for sequence problems
21	NN4	Recurrent Neural Networks and attention
22	NN5	RNNs in keras
23	Final Project Presentations
24	Final Project Presentations

Lecture notes