Homework 1 is out
Date | Topic | Book | Further Readings | |
---|---|---|---|---|
1 | Sep 01 |
NO CLASS (LORELEI) |
none | |
2 | Sep 03 |
NO CLASS (LORELEI) |
none | |
3 | Sep 08 |
Introduction / HMM review [pdf] |
none | |
4 | Sep 10 |
Writing / HMM review 2 [pdf] [pdf] [pdf] |
none | |
5 | Sep 15 |
Sequence Labeling with the Structured Perceptron [pdf] |
none | |
7 | Sep 17 |
Recitation / Questions (Kartik) |
none | |
6 | Sep 22 |
William Cohen Guest Lecture (EMNLP) [pdf] |
[readings] | |
8 | Sep 24 |
Decoding [pdf] |
LSP Ch. 2 | |
9 | Sep 29 |
Parsing [pdf] |
none | |
10 | Oct 01 |
Probability Distributions [pdf] |
none | |
11 | Oct 06 |
Soft Inference [pdf] |
LSP Ch. 5 | |
12 | Oct 08 |
Soft Inference 2 [pdf] |
none | |
13 | Oct 13 |
MBR Decoding [pdf] |
none | |
14 | Oct 15 |
Approximate Inference - Local Search and MCMC [pdf] |
none | |
15 | Oct 20 |
Lagrangian Relaxation [pdf] |
none | |
16 | Oct 22 |
Conditional Random Fields [pdf] |
LSP Ch. 3.3 | |
17 | Oct 27 |
Experimentation [pdf] |
none | |
18 | Oct 29 |
Empirical Risk Minimization [pdf] |
none | |
19 | Nov 03 |
Learning Generative Models [pdf] |
none | |
20 | Nov 05 |
Kartik Guest Lecture - Spectral Methods [pdf] [demo] |
none | |
21 | Nov 10 |
Expectation Maximization |
none | |
22 | Nov 12 |
Margin based learning with cost functions [pdf] |
none | |
23 | Nov 17 |
Neural Networks for structured prediction 1 [pdf] |
none | |
24 | Nov 19 |
Neural Networks for structured prediction 2 [pdf] |
none | |
25 | Nov 24 |
Unsupervised Learning with Features |
none | |
26 | Nov 26 |
THANKSGIVING |
none | |
27 | Dec 01 |
Edward Greffenstette guest lecture [pdf] |
none | |
28 | Dec 03 |
Peer paper review clinic |
none | |
29 | Dec 08 |
Neural sequence models - RNNs and LSTMs |
none | |
30 | Dec 10 |
Neural Sequence Models - CTC |
none |
Unless otherwise indicated, this content has been adapted from this course by Chris Dyer. Both the original and new content are licensed under a Creative Commons Attribution 3.0 Unported License. |