Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012"

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** '''Assignment due: memory-efficient SGD'''
 
** '''Assignment due: memory-efficient SGD'''
 
** ''New Assignment: Subsampling and visualizing a graph.'' [http://www.cs.cmu.edu/~wcohen/10-605/assignments/snowball.pdf PDF writeup]
 
** ''New Assignment: Subsampling and visualizing a graph.'' [http://www.cs.cmu.edu/~wcohen/10-605/assignments/snowball.pdf PDF writeup]
* Thus Mar 22. Semi-supervised learning via label propagation on graphs
+
* Thus Mar 22. [[Class meeting for 10-605 2012 03 20|Semi-supervised learning via label propagation on graphs]]
 
* Tues Mar 27. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
 
* Tues Mar 27. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
 
** '''Assignment due: Subsampling and visualizing a graph.'''
 
** '''Assignment due: Subsampling and visualizing a graph.'''

Revision as of 09:49, 20 March 2012

This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012.

January

February

March

  • Thus Mar 1. Guest Lecture: Ben van Durme, JHU, Randomized Algorithms for Large-Scale Learning
  • Tues Mar 6. Learning on graphs 2.
    • Hadoop assignments due
    • New Assignment: memory-efficient SGD PDF writeup
    • New assignment: initial project proposals. PDF writeup
  • Thus Mar 8. Guest Lecture: Joey Gonzales, CMU, GraphLab and Dynamic Asynchronous Computation PPT slides
  • Tues Mar 13. no class - spring break.
  • Thus Mar 15. no class - spring break.
  • Tues Mar 20. Subsampling a graph with RWR
    • Assignment due: initial mini-project proposals.
    • Assignment due: memory-efficient SGD
    • New Assignment: Subsampling and visualizing a graph. PDF writeup
  • Thus Mar 22. Semi-supervised learning via label propagation on graphs
  • Tues Mar 27. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
    • Assignment due: Subsampling and visualizing a graph.
    • New Assignment: mini-project proposals (final version)
  • Thus Mar 29. Understanding spectral clustering techniques.
    • Assignment due: mini-project proposals (final version).

April

  • Tues Apr 3. LDA-like models for text and graphs.
  • Thus Apr 5. Tentative: Guest lecture by U Kang, CMU.
  • Tues Apr 10. Speeding up LDA-like models: sampling and parallelization
  • Thus Apr 12. Fast KNN and similarity joins 1.
  • Tues Apr 17. Fast KNN and similarity joins 2.
  • Thus Apr 19. no class - Carnival
  • Tues Apr 24. Online LDA
  • Thus Apr 26. TBD.

May

  • Tues May 1. Project reports.
  • Thus May 3. Project reports.