Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015"
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* Tues Feb 10. '''student presentations''' | * Tues Feb 10. '''student presentations''' | ||
** '''HW2 due: phrase finding with stream-and-sort''' | ** '''HW2 due: phrase finding with stream-and-sort''' | ||
| − | ** ''HW3,4: Naive Bayes with Streaming Hadoop, Naive Bayes with Hadoop & Phrase-finding with Hadoop''. [http://curtis.ml.cmu.edu/w/courses/images/c/c0/Homework4a.pdf PDF Handout | + | ** ''HW3,4: Naive Bayes with Streaming Hadoop, Naive Bayes with Hadoop & Phrase-finding with Hadoop''. [http://curtis.ml.cmu.edu/w/courses/images/c/c0/Homework4a.pdf PDF Handout HW4 - warmup] |
| − | [http://curtis.ml.cmu.edu/w/courses/images/a/a2/Homework4b.pdf PDF Handout | + | [http://curtis.ml.cmu.edu/w/courses/images/a/a2/Homework4b.pdf PDF Handout HW4] |
| − | [http://curtis.ml.cmu.edu/w/courses/images/3/30/Homework4c.pdf PDF Handout | + | [http://curtis.ml.cmu.edu/w/courses/images/3/30/Homework4c.pdf PDF Handout HW5] |
* Thus Feb 12. '''student presentations''' | * Thus Feb 12. '''student presentations''' | ||
* Tues Feb 17. [[Class meeting for 10-605 SGD and Hash Kernels|Scalable SGD and Hash Kernels]] | * Tues Feb 17. [[Class meeting for 10-605 SGD and Hash Kernels|Scalable SGD and Hash Kernels]] | ||
Revision as of 16:49, 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.
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 and records 1; Phrase finding.
- Tues Jan 27. Phrase Finding and Rocchio
- HW1A and HW1B due.
- HW2: phrase finding with stream-and-sort. PDF Handout
- Thus Jan 29. Rocchio and 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 Handout HW4 - warmup
PDF Handout HW4 PDF Handout 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)
- 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
- 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.
April
- Thus Apr 2. Speeding up LDA-like models: All-reduce and online LDA
- Tues Apr 7. Workflows in PIG
- Thus Apr 9. Fast KNN and similarity joins
- Tues Apr 14. Parallel/Scalable Similarity Joins
- New Assignment: Workflows with Pig PDF handout
- Thus Apr 16. no class : carnival
- Tues Apr 21. Graph models for large-scale ML
- Assignment due: Workflows with Pig
- Thus Apr 23.
- Tues Apr 28. Exam review session.
- Thus Apr 30. In-class exam.
