Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013"
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* Mon Mar 25. [[Class meeting for 10-605 2013 03 25|Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs]] | * Mon Mar 25. [[Class meeting for 10-605 2013 03 25|Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs]] | ||
* Wed Mar 27. [[Class meeting for 10-605 2013 03 27|Understanding spectral clustering techniques.]] | * Wed Mar 27. [[Class meeting for 10-605 2013 03 27|Understanding spectral clustering techniques.]] | ||
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** '''Assignment due: mini-project proposals (final version).''' | ** '''Assignment due: mini-project proposals (final version).''' | ||
| − | |||
== April and May == | == April and May == | ||
* Mon Apr 1. [[Class meeting for 10-605 2013 04 01|LDA-like models for text and graphs]] | * Mon Apr 1. [[Class meeting for 10-605 2013 04 01|LDA-like models for text and graphs]] | ||
* Wed Apr 3. ''To be decided'' | * Wed Apr 3. ''To be decided'' | ||
| + | ** '''Assignment due: Subsampling and visualizing a graph.''' | ||
| + | ** ''New Assignment: K-Means on MapReduce.'' [www.cs.cmu.edu/~wcohen/10-605/assignments/kmeans.pdf PDF writeup] | ||
* Mon Apr 8. [[Class meeting for 10-605 2013 04 08|Speeding up LDA-like models: sampling and parallelization]] | * Mon Apr 8. [[Class meeting for 10-605 2013 04 08|Speeding up LDA-like models: sampling and parallelization]] | ||
* Wed Apr 10. [[Class meeting for 10-605 2013 04 10|Fast KNN and similarity joins 1.]] | * Wed Apr 10. [[Class meeting for 10-605 2013 04 10|Fast KNN and similarity joins 1.]] | ||
| Line 58: | Line 58: | ||
** '''Project progress report due''' | ** '''Project progress report due''' | ||
* Wed Apr 17. [[Class meeting for 10-605 2013 04 17|Scaling up decision tree learning]] | * Wed Apr 17. [[Class meeting for 10-605 2013 04 17|Scaling up decision tree learning]] | ||
| + | ** '''Assignment due: K-Means on MapReduce.''' | ||
* Mon Apr 22. [[Class meeting for 10-605 2013 04 22|SGD for matrix factorization and online LDA]] | * Mon Apr 22. [[Class meeting for 10-605 2013 04 22|SGD for matrix factorization and online LDA]] | ||
* Wed Apr 24. ''Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.'' | * Wed Apr 24. ''Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.'' | ||
Revision as of 11:32, 20 March 2013
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. no class - Martin Luther King Day
- Wed Jan 23. Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.
- New Assignment: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- 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.
- Mon Feb 11. Map-reduce and Hadoop 2.
- Wed Feb 13. Hadoop helpers and Scalable SGD
- Assignment due: phrase finding with stream-and-sort
- New Assignments: Naive Bayes with Hadoop & Phrase-finding with Hadoop. PDF Handout
- Mon Feb 18. Scalable SGD and Hash Kernels
- Wed Feb 20. Guest lecture: Chris Dyer. Scalable feature selection with Map-Reduce
- Streaming run on Hadoop of Naive Bayes due - checkpoint
- Mon Feb 25. Background on randomized algorithms; Graph computations 1.
- Wed Feb 27. Guest Lecture: Aapo Kyrola - GraphLab and GraphChi
- Hadoop assignment (Naive Bayes) due
March
- Mon Mar 4. Learning on graphs 2.
- Wed Mar 6. Guest lecture: John Wong (Google): Machine Learning with Large Datasets in Google Shopping"
- Hadoop assignment (phrase-finding) due
- New Assignment: memory-efficient SGD PDF writeup
- New assignment: initial project proposals. PDF writeup
- Mon Mar 11. no class - spring break.
- Wed Mar 13. no class - spring break.
- Mon Mar 18. Subsampling a graph with RWR
- Wed Mar 20. Semi-supervised learning via label propagation on graphs
- Assignment due: initial mini-project proposals.
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph. PDF writeup - draft!
- Mon Mar 25. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
- 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
- Wed Apr 3. To be decided
- Assignment due: Subsampling and visualizing a graph.
- New Assignment: K-Means on MapReduce. [www.cs.cmu.edu/~wcohen/10-605/assignments/kmeans.pdf PDF writeup]
- 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.
- Project progress report due
- Wed Apr 17. Scaling up decision tree learning
- Assignment due: K-Means on MapReduce.
- Mon Apr 22. SGD for matrix factorization and online LDA
- Wed Apr 24. Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.
- 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 (minimum 5 pp, up to 8pp double column), except, of course, do not submit it anonymously.
