Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015"
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* Tues Jan 13. [[Class meeting for 10-605 Overview|Overview of course, cost of various operations, asymptotic analysis.]] | * Tues Jan 13. [[Class meeting for 10-605 Overview|Overview of course, cost of various operations, asymptotic analysis.]] | ||
* Thus Jan 15. [[Class meeting for 10-605 Probability Review|Review of probabilities, joint distributions and naive Bayes]] | * Thus Jan 15. [[Class meeting for 10-605 Probability Review|Review of probabilities, joint distributions and naive Bayes]] | ||
| − | ** ''HW1A: streaming Naive Bayes 1 (with feature counts in memory)''. [http:// | + | ** ''HW1A: streaming Naive Bayes 1 (with feature counts in memory)''. [http://www.cs.cmu.edu/~yipeiw/TA605/hw1A/hashtable-nb_s15.pdf PDF Handout] |
* Tues Jan 20. [[Class meeting for 10-605 Streaming Naive Bayes|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.]] | * Tues Jan 20. [[Class meeting for 10-605 Streaming Naive Bayes|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.]] | ||
** ''HW1B: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort''. [http://curtis.ml.cmu.edu/w/courses/images/0/0d/Stream-nb.pdf PDF Handout] | ** ''HW1B: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort''. [http://curtis.ml.cmu.edu/w/courses/images/0/0d/Stream-nb.pdf PDF Handout] | ||
Revision as of 19:28, 15 January 2015
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015.
Notes:
- The assignments posted are drafts based on the assignments from 2014, and will be modified over the course of the semester - some may be changed substantially.
- Lecture notes and/or slides will be (re)posted around the time of the lectures.
Contents
January
- Tues Jan 13. Overview of course, cost of various operations, asymptotic analysis.
- Thus Jan 15. Review of probabilities, joint distributions and naive Bayes
- HW1A: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- Tues Jan 20. Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.
- HW1B: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
- For 10/11-805 students: a one-paragraph summary of a recent research result you'd like to present is due.
- Thus Jan 22. Messages, records and workflows; Phrase finding.
- Tues Jan 27. Messages, records and workflows; Rocchio
- HW1A and HW1B due.
- HW2: phrase finding with stream-and-sort. PDF Handout (DRAFT)
- Thus Jan 29. Parallel Perceptrons
February
- Tues Feb 3. Perceptrons/Map-reduce and Hadoop.
- Thus Feb 5. Parallel Perceptrons.
- Tues Feb 10. student presentations
- HW2 due: phrase finding with stream-and-sort
- HW3,4: Naive Bayes with Streaming Hadoop, Naive Bayes with Hadoop & Phrase-finding with Hadoop. PDF Handouts: HW3 - warmup,HW3,HW4.
- Thus Feb 12. student presentations
- Tues Feb 17. Scalable SGD and Hash Kernels
- For 10/11-805 students: initial draft of project proposal is due.
- Thus Feb 19. Matrix Factorization and SGD
- Tues Feb 24. SGD for Matrix Factorization, and Randomized Algorithms 1 (Bloom Filters)
- HW3 due: Naive Bayes with Hadoop
- Thus Feb 26. Randomized Algorithms
March
- Tues Mar 3. student presentations
- Thus Mar 5. student presentations
- HW4 due: Phrase-finding with Hadoop
- HW5: memory-efficient SGD PDF handout
- For 10/11-805 students: project proposal is due. This must contain a complete description of the data you will use.
- Tues Mar 10. no class - spring break.
- Thus Mar 12. no class - spring break.
- Tues Mar 17. Scalable PageRank
- HW5 due: memory-efficient SGD
- HW6: Subsampling and visualizing a graph. PDF handout
- Thus Mar 19. Subsampling a graph with RWR
- Tues Mar 24. Subsamping continued and SSL on Graphs AAAI Spring Symposium week
- Thus Mar 26. Scalable spectral clustering techniques. AAAI Spring Symposium week
- Tues Mar 31. Sparse sampling and parallelization for LDA
- HW6 due: Subsampling and visualizing a graph.
- HW7: TBA
April and May
- Thus Apr 2. Speeding up LDA-like models: All-reduce and online LDA
- Tues Apr 7. student presentations
- Thus Apr 9. student presentations
- Tues Apr 14. Scalable Similarity Joins
- HW7 due
- HW8: TBA
- Thus Apr 16. no class : carnival
- Tues Apr 21. Graph models for large-scale ML
- Thus Apr 23. Poster session for 10/11-805 projects
- Tues Apr 28. Exam review session.
- Thus Apr 30. In-class exam.
- Tues May 5.
- For 10/11-805 students: project reports are due
