Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012"
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* Thus Jan 19. [[Class meeting for 10-605 2012 01 19|Review of probabilities.]] | * Thus Jan 19. [[Class meeting for 10-605 2012 01 19|Review of probabilities.]] | ||
* Tues Jan 24. Streaming algorithms and Naive Bayes. | * Tues Jan 24. Streaming algorithms and Naive Bayes. | ||
− | ** '''Assignment: streaming Naive Bayes 1 (with feature counts in memory)''' | + | ** '''New Assignment: streaming Naive Bayes 1 (with feature counts in memory)''' |
* Thus Jan 26. The stream-and-sort design pattern; Naive Bayes revisited. | * Thus Jan 26. The stream-and-sort design pattern; Naive Bayes revisited. | ||
* Tues Jan 31. Messages and records 1; Phrase finding. | * Tues Jan 31. Messages and records 1; Phrase finding. | ||
+ | ** '''Assignment (due): streaming Naive Bayes 1 (with feature counts in memory)''' | ||
** '''Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort''' | ** '''Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort''' | ||
Revision as of 13:27, 17 January 2012
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012.
Contents
January
- Tues Jan 17. Overview of course, cost of various operations, asymptotic analysis.
- Thus Jan 19. Review of probabilities.
- Tues Jan 24. Streaming algorithms and Naive Bayes.
- New Assignment: streaming Naive Bayes 1 (with feature counts in memory)
- Thus Jan 26. The stream-and-sort design pattern; Naive Bayes revisited.
- Tues Jan 31. Messages and records 1; Phrase finding.
- Assignment (due): streaming Naive Bayes 1 (with feature counts in memory)
- Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort
February
- Thus Feb 2. Messages and records 2; Phrase finding.
- Tues Feb 7. Other streaming algorithms: voted perceptron, Rocchio; averaging.
- Assignment: phrase finding with stream-and-sort
- Thus Feb 9. Map-reduce and Hadoop 1 (Alona lecture).
- Tues Feb 14. Map-reduce and Hadoop 2. (Alona lecture).
- Assignment: Naive Bayes with Hadoop
- Thus Feb 16. Naive Bayes and Logistic regression.
- Tues Feb 21. Logistic regression with stochastic gradient descent.
- Assignment: Phrase-finding with Hadoop
- Thus Feb 23. Other SGD algorithms; parallelizing SGD.
- Tues Feb 28. Bloom Filters and Locality sensitive hashing 1.
- Assignment: memory-efficient SGD
March
- Thus Mar 1. Bloom Filters and Locality sensitive hashing 2.
- Tues Mar 6. Learning on graphs. PageRank, Harmonic field, RWR.
- Assignment: mini-project proposals 1.
- Thus Mar 8. Tools and design patterns for graphs (Pregel, GraphLab, Schimmy, ...)
- Tues Mar 13. no class - spring break.
- Thus Mar 15. no class - spring break.
- Tues Mar 20. Spectral clustering and PIC.
- Assignment: Subsampling and visualizing a graph.
- Thus Mar 22. Gibbs sampling and LDA 1.
- Tues Mar 27. Gibbs sampling and LDA 2.
- Assignment: mini-project proposals 2.
- Thus Mar 29. KNN classification and inverted indices.
- Assignment: mini-project proposals 2 are due.
April
- Tues Apr 3. Decision trees and random forests 1.
- Thus Apr 5. Decision trees and random forests 2.
- Tues Apr 10. Soft joins with KNN and inverted indices 1.
- Thus Apr 12. Soft joins with KNN and inverted indices 1.
- Tues Apr 17. Structured prediction 1.
- Thus Apr 19. no class - Carnival
- Tues Apr 24. Structured prediction 2.
- Thus Apr 26. Additional topics.
May
- Tues May 1. Project reports.
- Thus May 3. Project reports.