Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013"
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* Mon Feb 18. [[Class meeting for 10-605 2013 02 18|Scalable SGD and Hash Kernels]] | * Mon Feb 18. [[Class meeting for 10-605 2013 02 18|Scalable SGD and Hash Kernels]] | ||
* Wed Feb 20. ''To be decided'' | * Wed Feb 20. ''To be decided'' | ||
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* Mon Feb 25. [[Class meeting for 10-605 2013 02 25|Background on randomized algorithms; Graph computations 1.]] | * Mon Feb 25. [[Class meeting for 10-605 2013 02 25|Background on randomized algorithms; Graph computations 1.]] | ||
* Wed Feb 27. ''To be decided'' | * Wed Feb 27. ''To be decided'' | ||
Revision as of 19:23, 18 December 2012
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013.
Contents
January
- Mon Jan 14. Overview of course, cost of various operations, asymptotic analysis.
- Wed Jan 16. Review of probabilities.
- Mon Jan 21. Streaming algorithms and Naive Bayes.
- New Assignment: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- Wed Jan 23. The stream-and-sort design pattern; Naive Bayes revisited.
- Mon Jan 28. Messages and records 1; Phrase finding.
- Assignment due: streaming Naive Bayes 1 (with feature counts in memory).
- New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
- Wed Jan 30. More on streaming algorithms: Rocchio, and theory of on-line learning
February
- Mon Feb 4. More on streaming algorithms: parallelized voted perceptrons.
- Assignment due: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort
- New Assignment: phrase finding with stream-and-sort. PDF Handout
- Wed Feb 6. Map-reduce and Hadoop 1 (Alona lecture).
- Mon Feb 11. Map-reduce and Hadoop 2. (Alona lecture, William is closer).
- Assignment due 2/15: phrase finding with stream-and-sort
- New Assignment: Naive Bayes with Hadoop & Phrase-finding with Hadoop PDF Handout
- Wed Feb 13. Hadoop helpers and Scalable SGD
- Mon Feb 18. Scalable SGD and Hash Kernels
- Wed Feb 20. To be decided
- Mon Feb 25. Background on randomized algorithms; Graph computations 1.
- Wed Feb 27. To be decided
March
- Mon Mar 4. Learning on graphs 2.
- Hadoop assignments due
- New Assignment: memory-efficient SGD PDF writeup
- New assignment: initial project proposals. PDF writeup
- Wed Mar 6. To be decided
- Mon Mar 11. no class - spring break.
- Wed Mar 13. no class - spring break.
- Mon Mar 18. 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
- Wed Mar 20. Semi-supervised learning via label propagation on graphs
- Mon Mar 25. 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)
- Wed Mar 27. Understanding spectral clustering techniques.
- Assignment due: mini-project proposals (final version).
April and May
- Mon Apr 1. LDA-like models for text and graphs; guest lecture from Partha Talukdar
- Wed Apr 3. To be decided
- Mon Apr 8. Speeding up LDA-like models: sampling and parallelization
- Wed Apr 10. Fast KNN and similarity joins 1.
- Mon Apr 15. Fast KNN and similarity joins 2.
- Wed Apr 17. To be decided
- Mon Apr 22. SGD for matrix factorization and online LDA
- Wed Apr 24. Scaling up decision tree learning
- Mon Apr 29 Project reports.
- Wed May 1. Project reports.
May
- Fri May 3.
- Project writeups due at 5:00pm. Submit a paper to Blackbook in PDF in the ICML 2013 format (up to 8pp double column), except, of course, do not submit it anonymously.
