Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015"
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** ''New Assignment: Subsampling and visualizing a graph.'' [http://curtis.ml.cmu.edu/w/courses/images/e/eb/ApproxPageRank.pdf PDF handout] | ** ''New Assignment: Subsampling and visualizing a graph.'' [http://curtis.ml.cmu.edu/w/courses/images/e/eb/ApproxPageRank.pdf PDF handout] | ||
| − | == April | + | == April == |
| − | * | + | * Thus Apr 2. [[Class meeting for 10-605 2013 LDA 2|Speeding up LDA-like models: All-reduce and online LDA]] |
| − | * | + | * Tues Apr 7. [[Class meeting for 10-605 PIG|Workflows in PIG]] |
| − | * | + | * Thus Apr 9. [[Class meeting for 10-605 Similarity Joins|Fast KNN and similarity joins]] |
| − | * | + | * Tues Apr 14. [[Class meeting for 10-605 Parallel Similarity Joins|Parallel/Scalable Similarity Joins]] |
** '''Assignment due: Subsampling and visualizing a graph.''' | ** '''Assignment due: Subsampling and visualizing a graph.''' | ||
** ''New Assignment: Workflows with Pig'' [http://curtis.ml.cmu.edu/w/courses/images/4/46/Nb_pig.pdf PDF handout] | ** ''New Assignment: Workflows with Pig'' [http://curtis.ml.cmu.edu/w/courses/images/4/46/Nb_pig.pdf PDF handout] | ||
| − | * | + | * Thus Apr 16. ''no class : carnival'' |
| − | * | + | * Tues Apr 21. [[Class meeting for 10-605 GraphLab|Graph models for large-scale ML]] |
| − | |||
** '''Assignment due: Workflows with Pig''' | ** '''Assignment due: Workflows with Pig''' | ||
| − | * | + | * Thus Apr 23. |
| + | * Tues Apr 28. Exam review session. | ||
** [http://curtis.ml.cmu.edu/w/courses/images/0/0a/Practice_questions.pdf PDF practice questions] | ** [http://curtis.ml.cmu.edu/w/courses/images/0/0a/Practice_questions.pdf PDF practice questions] | ||
** [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. |
| + | |||
| + | Not happening this year: | ||
| + | * [[Class meeting for 10-605 First-Order Logics|First-order logics]] | ||
| + | * Thus Apr 21. [[Class meeting for 10-605 Scalable FOL|Scalable First-order logics]] | ||
Revision as of 12:19, 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
- Tues Jan 20. 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
- Thus Jan 22. Messages and records 1; Phrase finding.
- Assignment due: streaming Naive Bayes 1 (with feature counts in memory).
- Tues Jan 27. Phrase Finding and Rocchio
- New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
- Thus Jan 29. Rocchio and Parallel Perceptrons
February
- Tues Feb 3. Perceptrons/Map-reduce and Hadoop.
- 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
- Thus Feb 5. Parallel Perceptrons.
- Tues Feb 10. student presentations
- Thus Feb 12. student presentations
- Tues Feb 17. Scalable SGD and Hash Kernels
- Assignment due: phrase finding with stream-and-sort
- New Assignments: Naive Bayes with Streaming Hadoop, Naive Bayes with Hadoop & Phrase-finding with Hadoop. PDF Handout (4a)PDF Handout (4b)PDF Handout (4c)
- Thus Feb 19. Matrix Factorization and SGD, plus another Hadoop demo
- Tues Feb 24. SGD for Matrix Factorization, and Randomized Algorithms 1 (Bloom Filters)
- Streaming run on Hadoop of Naive Bayes due
- Thus Feb 26. Randomized Algorithms
- Non-streaming run on Hadoop of Naive Bayes due.
March
- Tues Mar 3. student presentations
- Thus Mar 5. student presentations
- Fri Mar 6.
- Hadoop assignment (phrase-finding) due
- Tues Mar 10. no class - spring break.
- Thus Mar 12. no class - spring break.
- Tues Mar 17. Scalable PageRank
- New Assignment: memory-efficient SGD 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
- Assignment due: memory-efficient SGD
- Tues Mar 31. Sparse sampling and parallelization for LDA
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph. PDF handout
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
- Assignment due: Subsampling and visualizing a graph.
- 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.
Not happening this year:
- First-order logics
- Thus Apr 21. Scalable First-order logics
