| Date | Topic | Book | Further Readings | Lecture Slides |
|---|---|---|---|---|
| Jan 17 |
Introduction 1 |
none |
[pdf] |
|
| Jan 22 |
Introduction 2 |
none | ||
| Jan 24 |
HMM review |
none |
[pdf] |
|
| Jan 29 |
Decoding |
none |
[pdf] |
|
| Jan 31 |
Recitation / Questions (Chaitanya) Inference (Prof. Taylor) |
none |
[pdf] [pdf] |
|
| Feb 5 |
Multi Document Summarization |
none |
[pdf] |
|
| Feb 7 |
Approches to Inference |
none |
[pdf] |
|
| Feb 12 |
Probability |
LSP Ch. 2 |
[pdf] |
|
| Feb 14 |
Natural Language Parsing with Context-Free Grammars |
none |
[pdf] [pdf] |
|
| Feb 19 |
Natural Language Dependency Parsing |
none |
[pdf] |
|
| Feb 21 |
Soft Inference |
LSP Ch. 5 |
[pdf] |
|
| Feb 26 |
Soft Inference 2 |
none |
[pdf] |
|
| Feb 28 |
Recitation 2 by Chaitanya |
none |
[pdf] |
|
| Mar 7 |
MBR Decoding |
none |
[pdf] |
|
| Mar 19 |
Approximate Inference - Local Search and MCMC |
none |
[pdf] | |
| Mar 26 |
Lagrangian Relaxation |
none |
[pdf] |
|
| Mar 28 |
Conditional Random Fields |
LSP Ch. 3.3 |
[pdf] |
|
| Apr 2 |
Experimentation and Empirical Risk Minimization |
none |
[pdf] [pdf] |
|
| Apr 4 |
Learning Generative Models |
none | ||
| Apr 9 |
Expectation Maximization |
none |
[pdf] [pdf] |
|
| Apr 11 |
Structure and Support Vector Machines |
none |
[pdf] |
|
| Apr 16 |
Neural Networks for structured prediction 1 |
none | ||
| Apr 18 |
Neural Networks for structured prediction 2 |
none | ||
| Apr 23 |
Unsupervised Learning with Features |
none | ||
| Apr 25 |
Neural CRF (Chaitanya) |
none |
[pdf] |
|
| Apr 30 |
Neural Sequence Models - CTC |
none | ||
| May 2 |
TBA |
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. |