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. |