Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014"

Revision as of 14:55, 30 January 2014

Notes:

The assignments are from 2013, and will be modified over the course of the semester - some may be changed substantially.
Lecture notes will be posted around the time of the lectures.

Mon Jan 13. Overview of course, cost of various operations, asymptotic analysis.
Wed Jan 15. Review of probabilities, joint distributions and naive Bayes
Mon Jan 20. No class - 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).
Wed Jan 29. Phrase Finding and Rocchio
- New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
Thursday Jan 30. Scheduled down-time for the wiki host (should only be one day).

Mon Feb 3. Rocchio and Parallel 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.
Mon Feb 10. More Hadoop and Scaleable SGD.
Wed Feb 12. Guest lecture: Matt Hurst, Microsoft/Bing: Local Search at Bing
- 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 and 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

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
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.
Mon Mar 31. Sparse sampling and parallelization for LDA

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.
  - PDF writeup of image-classification assignment
  - PDF writeup of scalable classification
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.

@@ Line 49: / Line 49: @@
 * Mon Mar 24. [[Class meeting for 10-605 SSL LP 2|Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs]]
 * Wed Mar 26. [[Class meeting for 10-605 Spectral Clustering|Understanding spectral clustering techniques.]]
-* Wed Mar 31. [[Class meeting for 10-605 LDA 1|Sparse sampling and parallelization for LDA]]
+* Mon Mar 31. [[Class meeting for 10-605 LDA 1|Sparse sampling and parallelization for LDA]]
 == April and May ==