Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013"
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** ''New Assignment: Multi-class image classification or scalable classification using a linear classifier.'' Both of these count as one assignment toward your six. | ** ''New Assignment: Multi-class image classification or scalable classification using a linear classifier.'' Both of these count as one assignment toward your six. | ||
*** [http://www.cs.cmu.edu/~wcohen/10-605/assignments/image.pdf PDF writeup of image-classification assignment] | *** [http://www.cs.cmu.edu/~wcohen/10-605/assignments/image.pdf PDF writeup of image-classification assignment] | ||
| − | *** | + | *** [http://www.cs.cmu.edu/~wcohen/10-605/assignments/big-classifier.pdf PDF writeup of scalable classification] |
* Mon Apr 22. ''Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.'' | * Mon Apr 22. ''Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.'' | ||
* Wed Apr 24. Project reports. | * Wed Apr 24. Project reports. | ||
* Mon Apr 29. Project reports. | * Mon Apr 29. Project reports. | ||
* Wed May 1. Project reports. | * Wed May 1. Project reports. | ||
| − | ** ''Assignment due: Multi-class image classification or scalable classification.'' | + | ** '''Assignment due: Multi-class image classification or scalable classification.''' |
| − | ** '''Assignment due: scalable classification'' | + | ** ''''Assignment due: scalable classification''' |
== May == | == May == | ||
Revision as of 15:06, 16 April 2013
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013.
Contents
January
- Mon Jan 14. Overview of course, cost of various operations, asymptotic analysis.
- Wed Jan 16. Review of probabilities.
- Mon Jan 21. no class - Martin Luther King Day
- Wed Jan 23. 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 28. 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 30. More on streaming algorithms: Rocchio, and theory of on-line learning
February
- Mon Feb 4. 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 6. Map-reduce and Hadoop 1.
- Mon Feb 11. Map-reduce and Hadoop 2.
- Wed Feb 13. 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 18. Scalable SGD and Hash Kernels
- Wed Feb 20. Guest lecture: Chris Dyer. Scalable feature selection with Map-Reduce
- Streaming run on Hadoop of Naive Bayes due - checkpoint
- Mon Feb 25. Background on randomized algorithms; Graph computations 1.
- Wed Feb 27. Guest Lecture: Aapo Kyrola - GraphLab and GraphChi
- Hadoop assignment (Naive Bayes) due
March
- Mon Mar 4. Learning on graphs 2.
- Wed Mar 6. Guest lecture: John Wong (Google): Machine Learning with Large Datasets in Google Shopping"
- Hadoop assignment (phrase-finding) due
- New Assignment: memory-efficient SGD PDF writeup
- New assignment: initial project proposals. PDF writeup
- Mon Mar 11. no class - spring break.
- Wed Mar 13. no class - spring break.
- Mon Mar 18. Subsampling a graph with RWR
- Wed Mar 20. Semi-supervised learning via label propagation on graphs
- Assignment due: initial mini-project proposals.
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph. PDF writeup
- Mon Mar 25. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
- Wed Mar 27. Understanding spectral clustering techniques.
- Assignment due: mini-project proposals (final version).
April and May
- Mon Apr 1. Speeding up LDA-like models: sparse sampling and parallelization
- Wed Apr 3. 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 8. Fast KNN and similarity joins 1.
- Wed Apr 10. Fast KNN and similarity joins 2.
- Mon Apr 15. Scaling up decision tree learning
- Project progress report due
- Wed Apr 17. SGD for matrix factorization
- Assignment due: K-Means on MapReduce.
- New Assignment: Multi-class image classification or scalable classification using a linear classifier. Both of these count as one assignment toward your six.
- Mon Apr 22. Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.
- Wed Apr 24. Project reports.
- Mon Apr 29. Project reports.
- Wed May 1. Project reports.
- Assignment due: Multi-class image classification or scalable classification.
- 'Assignment due: scalable classification
May
- Fri May 3.
- Project writeups due at 5:00pm. Submit a paper to Blackbook in PDF in the ICML 2013 format (minimum 5 pp, up to 8pp double column), except, of course, do not submit it anonymously.
