List of Readings

This list is the same as in the course description. However, we will be update this as we go along in the course, with your own suggestions for relevant papers also included.

Please note that these are suggested readings. You are not expected to read all of them. Readings with * are highly recommendable, and those with ** are to be presented by YOU! (refer to 'Presentations' for details and schedule)

Note: Readings that do not have a link either refer to the books or must be googled.

April 8 - Exam 1

Manifold Learning, Apr 22

Novelty Detection, Apr 22

Probabilistic Modeling, Apr 23,29

April 30 - Project Progress Report 2

Active learning, Apr 30

Fisher Kernel, May 6

Data Fusion, May 7

Multi-task Learning, May 13

KCCA, May 14

  • Roland will talk about KCCA
  • Remainder of May - Biological applications and Project discussions

May 27 - Final Project Paper

May 27,28 - Project Presentations

**** First half below ****

Introduction, Feb 12

Optimization, Feb 13

Kernel Methods, Feb 19,20

Support Vector Machines, Feb 26,27

Faster SVMs, Mar 5 - Presented by Konstantin

Support Vector Regression, Mar 11

Ranking + Semi-supervised Regression, Mar 12

Nu SVMs + SMO, Mar 18

Evaluative Methods, Mar 19

Boosting, Mar 25

Principal Component Analysis, Mar 25

Clustering and Spectral Clustering, Apr 1,2,15

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