Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014"
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** '''Assignment due: Subsampling and visualizing a graph.''' | ** '''Assignment due: Subsampling and visualizing a graph.''' | ||
** ''New Assignment: K-Means on MapReduce.'' [http://www.cs.cmu.edu/~wcohen/10-605/assignments/kmeans.pdf PDF writeup] | ** ''New Assignment: K-Means on MapReduce.'' [http://www.cs.cmu.edu/~wcohen/10-605/assignments/kmeans.pdf PDF writeup] | ||
| − | * Mon Apr 7. [[Class meeting for 10-605 | + | * Mon Apr 7. [[Class meeting for 10-605 Fast KNN 1|Fast KNN and similarity joins 1.]] |
| − | * Wed Apr 9. [[Class meeting for 10-605 | + | * Wed Apr 9. [[Class meeting for 10-605 Fast KNN 2|Fast KNN and similarity joins 2.]] |
| − | * Mon Apr 14. [[Class meeting for 10-605 | + | * Mon Apr 14. [[Class meeting for 10-605 Decision Trees|Scaling up decision tree learning]] |
| − | * Wed Apr 16. [[Class meeting for 10-605 | + | * Wed Apr 16. [[Class meeting for 10-605 Gradient Boosting|Gradient boosting with trees]] |
** '''Assignment due: K-Means on MapReduce.''' | ** '''Assignment due: K-Means on MapReduce.''' | ||
** ''New Assignment: Multi-class image classification or scalable classification using a linear classifier.'' | ** ''New Assignment: Multi-class image classification or scalable classification using a linear classifier.'' | ||
Revision as of 18:09, 8 January 2014
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014.
Contents
January
- Mon Jan 13. Overview of course, cost of various operations, asymptotic analysis.
- Wed Jan 15. Review of probabilities.
- Mon Jan 20. 'No class for Martin Luther King Day.
- Wed Jan 22. 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
- Mon Jan 27. Messages and records 1; Phrase finding.
- Assignment due: streaming Naive Bayes 1 (with feature counts in memory).
- New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
- Wed Jan 29. More on streaming algorithms: Rocchio, and theory of on-line learning
February
- Mon Feb 3. More on streaming algorithms: parallelized voted perceptrons.
- 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
- Wed Feb 5. Map-reduce and Hadoop 1.
- Mon Feb 10. Map-reduce and Hadoop 2.
- Wed Feb 12. Hadoop helpers and Scalable SGD
- Assignment due: phrase finding with stream-and-sort
- New Assignments: Naive Bayes with Hadoop & Phrase-finding with Hadoop. PDF Handout
- Mon Feb 17. Scalable SGD and Hash Kernels
- Wed Feb 19. Matrix Factorization ad SGD
- Streaming run on Hadoop of Naive Bayes due - checkpoint
- Mon Feb 24. Background on randomized algorithms; Graph computations 1.
- Wed Feb 26. Graphs computations 2
- Hadoop assignment (Naive Bayes) due
March
- Mon Mar 3. "Guest Lecture: Garth Gibson, topic TBA"
- Wed Mar 4. Guest lecture: Alex Beutel, SGD on Hadoop"
- Hadoop assignment (phrase-finding) due
- New Assignment: memory-efficient SGD PDF writeup
- New assignment: initial project proposals. PDF writeup
- Mon Mar 10. no class - spring break.
- Wed Mar 12. no class - spring break.
- Mon Mar 17. Subsampling a graph with RWR
- Wed Mar 19. Semi-supervised learning via label propagation on graphs
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph. PDF writeup
- Mon Mar 24. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
- Wed Mar 26. Understanding spectral clustering techniques.
- Wed Mar 31. Sparse sampling and parallelization for LDA
April and May
- Wed Apr 1. Speeding up LDA-like models: All-reduce and online LDA
- Assignment due: Subsampling and visualizing a graph.
- New Assignment: K-Means on MapReduce. PDF writeup
- Mon Apr 7. Fast KNN and similarity joins 1.
- Wed Apr 9. Fast KNN and similarity joins 2.
- Mon Apr 14. Scaling up decision tree learning
- Wed Apr 16. Gradient boosting with trees
- Assignment due: K-Means on MapReduce.
- New Assignment: Multi-class image classification or scalable classification using a linear classifier.
- Mon Apr 21. TBD
- Wed Apr 23. TBD
- Mon Apr 28. TBD
- Assignment due: Multi-class image classification or scalable classification.
- Wed Apr 30. In-class exam.
