Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012"
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* Thus Feb 16. [[Class meeting for 10-605 2012 02 16|Hadoop helpers and Scalable SGD]] | * Thus Feb 16. [[Class meeting for 10-605 2012 02 16|Hadoop helpers and Scalable SGD]] | ||
* Tues Feb 21. [[Class meeting for 10-605 2012 02 21|Scalable SGD and Hash Kernels]] | * Tues Feb 21. [[Class meeting for 10-605 2012 02 21|Scalable SGD and Hash Kernels]] | ||
| − | * Thus Feb 23. ''Guest lecture'': Ron Bekkerman, LinkedIn, Scaling up Machine Learning | + | * Thus Feb 23. ''Guest lecture'': [http://www.cs.umass.edu/~ronb/ Ron Bekkerman], LinkedIn, Scaling up Machine Learning |
** [http://www.cs.cmu.edu/~wcohen/10-605/2012-02-23-bekkerman.pptx Ron's slides in Powerpoint] | ** [http://www.cs.cmu.edu/~wcohen/10-605/2012-02-23-bekkerman.pptx Ron's slides in Powerpoint] | ||
** [http://www.cs.cmu.edu/~wcohen/10-605/2012-02-23-bekkerman.pdf Ron's slides in PDF] | ** [http://www.cs.cmu.edu/~wcohen/10-605/2012-02-23-bekkerman.pdf Ron's slides in PDF] | ||
| − | * Tues Feb 28. | + | * Tues Feb 28. [[Class meeting for 10-605 2012 02 28|Background on randomized algorithms; Graph computations 1.]] |
== March == | == March == | ||
Revision as of 11:57, 27 February 2012
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012.
Contents
January
- Tues Jan 17. Overview of course, cost of various operations, asymptotic analysis.
- Thus Jan 19. Review of probabilities.
- Tues Jan 24. Streaming algorithms and Naive Bayes.
- New Assignment: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- Thus Jan 26. The stream-and-sort design pattern; Naive Bayes revisited.
- Tues Jan 31. 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
February
- Thus Feb 2. More on streaming algorithms: Rocchio, and theory of on-line learning
- Tues Feb 7. 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
- Thus Feb 9. Map-reduce and Hadoop 1 (Alona lecture).
- Tues Feb 14. Map-reduce and Hadoop 2. (Alona lecture, William is closer).
- Assignment due 2/15: phrase finding with stream-and-sort
- New Assignment: Naive Bayes with Hadoop & Phrase-finding with Hadoop PDF Handout
- Thus Feb 16. Hadoop helpers and Scalable SGD
- Tues Feb 21. Scalable SGD and Hash Kernels
- Thus Feb 23. Guest lecture: Ron Bekkerman, LinkedIn, Scaling up Machine Learning
- Tues Feb 28. Background on randomized algorithms; Graph computations 1.
March
- Thus Mar 1. Guest Lecture: Ben van Durme, JHU, Randomized Algorithms for Large-Scale Learning
- Tues Mar 6. Learning on graphs. PageRank, Harmonic field, RWR; tools and design patterns for graphs (Pregel, GraphLab, Schimmy, ...)
- Hadoop assignments due
- New Assignment: memory-efficient SGD
- New assignment: mini-project proposals (first draft).
- Thus Mar 8. Guest Lecture: Joey Gonzales, CMU, GraphLab and Dynamic Asynchronous Computation
- Tues Mar 13. no class - spring break.
- Thus Mar 15. no class - spring break.
- Tues Mar 20. Spectral clustering and PIC.
- Assignment due: mini-project proposals (first draft).
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph.
- Thus Mar 22. Tentative: Guest lecture by U Kang, CMU.
- Tues Mar 27. Gibbs sampling and LDA.
- Assignment due: Subsampling and visualizing a graph.
- New Assignment: mini-project proposals (final version)
- Thus Mar 29. KNN classification and inverted indices.
- Assignment due: mini-project proposals (final version).
April
- Tues Apr 3. Decision trees and random forests 1.
- Thus Apr 5. Decision trees and random forests 2.
- Tues Apr 10. Soft joins with KNN and inverted indices 1.
- Thus Apr 12. Soft joins with KNN and inverted indices 1.
- Tues Apr 17. Structured prediction 1.
- Thus Apr 19. no class - Carnival
- Tues Apr 24. Structured prediction 2.
- Thus Apr 26. Additional topics.
May
- Tues May 1. Project reports.
- Thus May 3. Project reports.
