| 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 |
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| 14 |
Retrofitting |
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| 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 |
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| 24 |
Final Project Presentations |
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