Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015"
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Jump to navigationJump to search| Line 39: | Line 39: | ||
** ''HW5: memory-efficient SGD'' [http://curtis.ml.cmu.edu/w/courses/images/0/08/Sgd.pdf PDF handout] | ** ''HW5: memory-efficient SGD'' [http://curtis.ml.cmu.edu/w/courses/images/0/08/Sgd.pdf PDF handout] | ||
* Thus Mar 5. ''student presentations'' | * Thus Mar 5. ''student presentations'' | ||
| + | ** ''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.'' | * Tues Mar 10. ''no class - spring break.'' | ||
* Thus Mar 12. ''no class - spring break.'' | * Thus Mar 12. ''no class - spring break.'' | ||
| Line 51: | Line 52: | ||
** ''HW7: TBA'' | ** ''HW7: TBA'' | ||
| − | == April == | + | == April and May == |
* Thus Apr 2. [[Class meeting for 10-605 2013 LDA 2|Speeding up LDA-like models: All-reduce and online LDA]] | * Thus Apr 2. [[Class meeting for 10-605 2013 LDA 2|Speeding up LDA-like models: All-reduce and online LDA]] | ||
| − | * Tues Apr 7. | + | * Tues Apr 7. ''student presentations'' |
| − | * Thus Apr 9. | + | * Thus Apr 9. ''student presentations'' |
| − | * Tues Apr 14. [[Class meeting for 10-605 Parallel Similarity Joins| | + | * Tues Apr 14. [[Class meeting for 10-605 Parallel Similarity Joins|Scalable Similarity Joins]] |
** '''HW7 due''' | ** '''HW7 due''' | ||
** ''HW8: TBA'' | ** ''HW8: TBA'' | ||
* Thus Apr 16. ''no class : carnival'' | * Thus Apr 16. ''no class : carnival'' | ||
* Tues Apr 21. [[Class meeting for 10-605 GraphLab|Graph models for large-scale ML]] | * Tues Apr 21. [[Class meeting for 10-605 GraphLab|Graph models for large-scale ML]] | ||
| − | * Thus Apr 23. | + | * Thus Apr 23. ''Poster session for 10/11-805 projects'' |
* Tues Apr 28. Exam review session. | * Tues Apr 28. Exam review session. | ||
** '''HW8: due''' | ** '''HW8: due''' | ||
| Line 67: | Line 68: | ||
** [http://www.cs.cmu.edu/~wcohen/10-605/exam-review.pptx Review session slides] | ** [http://www.cs.cmu.edu/~wcohen/10-605/exam-review.pptx Review session slides] | ||
* Thus Apr 30. In-class exam. | * Thus Apr 30. In-class exam. | ||
| + | |||
| + | * Tues May 5. | ||
| + | ** ''For 10/11-805 students:'' '''project reports''' are due | ||
== Topics covered in previous years but not in 2015 == | == Topics covered in previous years but not in 2015 == | ||
| − | * | + | * [[Class meeting for 10-605 PIG|Workflows in PIG]] |
| − | * | + | * [[Class meeting for 10-605 Similarity Joins|Fast KNN and similarity joins]] |
| + | * [[Class meeting for 10-605 First-Order Logics|First-order logics]] | ||
| + | * [[Class meeting for 10-605 Scalable FOL|Scalable First-order logics]] | ||
Revision as of 17:25, 5 January 2015
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015.
Notes:
- The assignments are from 2014, and will be modified over the course of the semester - some may be changed substantially.
- Lecture notes and/or slides will be 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
- 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
- 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: HW4 - warmup,HW4,HW5.
- Thus Feb 12. student presentations
- Tues Feb 17. Scalable SGD and Hash Kernels
- HW3 due: Naive Bayes with Hadoop
- Thus Feb 19. Matrix Factorization and SGD, plus another Hadoop demo
- Tues Feb 24. SGD for Matrix Factorization, and Randomized Algorithms 1 (Bloom Filters)
- For 10/11-805 students: initial draft of project proposal is due.
- Thus Feb 26. Randomized Algorithms
March
- Tues Mar 3. student presentations
- HW4 due: Phrase-finding with Hadoop
- HW5: memory-efficient SGD PDF handout
- Thus Mar 5. student presentations
- 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
