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HW0: due Friday, Sept 8, on Gradescope.
(If you like: hw0.tex source)
- HW1: due at 11PM on Friday, Sept 22, on Gradescope.
You will need to download two files hw_1.py and hw_1.ipynb. You will also need to download
the data file, which is around 46 MB to download, and takes around 210 MB when unzipped.
(For reference: html format)
Make sure to reload this page to ensure you’re seeing the latest version.
Readings should be done before the indicated class.
- JM = Jurafasky and Martin, Speech and Language Processing, 3rd edition draft chapters
- Eis. = Eisenstein draft text
Tue 9/5 - Introduction [slides]
Tue 9/12 - N-Gram Language Models [slides]
Thu 9/14 - Classification: Naive Bayes
Tue 9/19 - Classification: Evaluation and Annotation [slides], [scan]
Thu 9/21 - Classification: Logistic regression [slides]
- In our class we'll use this perspective: logistic regression is a probabilistic model for classification.
The perceptron algorithm is one way to learn its parameters from labeled training data.
(Maximum likelihood, like we saw for LMs, NB and will for HMMs, is a different learning method.)
- JM ch. 7, Logistic Regression
- Daume ch. 3, The Perceptron
Tue 9/26 - Sequence Tagging: POS and HMMs
Thu 9/28 - Sequence Tagging: Viterbi