Embedded Files
and other readings
Slides only provide an outline of the details presented in lectures. They are not a replacement for class notes or readings from the textbook or other sources.
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Jan 22 - 24 : Introduction: regular expressions, tokens, and text normalization ( Jurafsky & Martin, Ch. 2 )
Jan 29 - 31 : N-gram Language models ( Jurafsky & Martin, Ch. 3 )
Feb 5 - 7 : Smoothing, backoff, and interpolation; perplexity and entropy ( Jurafsky & Martin, Ch. 3 )
Feb 12 - 14 : Text Classification ( Jurafsky & Martin, Ch. 4 )
slides | Thumbs up? Sentiment Classification using Machine Learning Techniques (Pang et al.; 2002)
Feb 19 - 21 : More on Classification: Linear Classifiers, Discriminative vs Generative Models, and Model Evaluation ( Jurafksy & Martin, Ch. 4 )
Unlimited extrapolation with limited data (and purpose!)
Feb 26 : Discriminative vs Generative Models, and Model Evaluation ( Jurafksy & Martin, Ch. 4 )
Feb 28 - Mar 6 : Logistic Regression ( Jurafksy & Martin, Ch. 5 )
--- Mar 11 - 15 : Spring Break ---
Mar 18 - 25 : Lexical and Vector semantics ( Jurafsky & Martin, Ch. 6 )
Mar 27 - Apr 3 : A prologue to neural networks: linear predictors and kernel methods
Please revise the material on neurons and the perceptron classifier (see the first few slides on discrimnative and generative classification)
Apr 8 - 10 : Feedforward neural network ( Jurafsky & Martin, Ch. 7 )
Apr 15 : Convolutions and filters: a whiteboard discussion with emphasis on its usage in the fourth assignment
A Primer on Neural Network Models for Natural Language Processing: Sec. 9 and 9.1 (Goldberg, Yoav; 2015)
Convolutional Neural Networks for Sentence Classification (Kim, Yoon; 2014)
Simple Deep Neural Networks for Text Classification : A YouTube Video containing explanations of convolutions (recommended by our graduate TA, Jasdeep Grover)
Apr 17 : An overview of PyTorch
Apr 22 - 24, 29 : Recurrent Neural Networks ( Jurafsky & Martin, Ch. 9 )
Apr 31 : Recurrent Neural Networks ( Jurafsky & Martin, Ch. 9 )
Apr 31 : Dialogue Agents and Ethics in NLP ( Jurafksy & Martin, Ch. 15: up to Sec. 15.3 )
slides by Daniel Jurafsky (the lecture included additional aspects of AI ethics, and did not *exactly* follow the order of these slides for the first sections of Ch. 15 in the textbook)
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