Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013

This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013.

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.
  • Assignment due: phrase finding with stream-and-sort
  • New Assignments: Naive Bayes with Hadoop & Phrase-finding with Hadoop PDF Handout
• Wed Feb 13. Hadoop helpers and Scalable SGD
• Mon Feb 18. Scalable SGD and Hash Kernels
  • Streaming run on Hadoop of Naive Bayes due - checkpoint
• Wed Feb 20. Guest lecture: Chris Dyer. Scalable feature selection with Map-Reduce.
• Mon Feb 25. Background on randomized algorithms; Graph computations 1.
  • Hadoop assignment (Naive Bayes) due
• Wed Feb 27. Guest Lecture: Aappo Kyrola - GraphLab and GraphChi

March

• Mon Mar 4. Learning on graphs 2.
  • Hadoop assignment (phrase-finding) due
  • New Assignment: memory-efficient SGD PDF writeup
  • New assignment: initial project proposals. PDF writeup
• Wed Mar 6. Guest lecture: John Wong (Google)
• Mon Mar 11. no class - spring break.
• Wed Mar 13. no class - spring break.
• Mon Mar 18. Subsampling a graph with RWR
  • Assignment due: initial mini-project proposals.
  • Assignment due: memory-efficient SGD
  • New Assignment: Subsampling and visualizing a graph. PDF writeup
• Wed Mar 20. Semi-supervised learning via label propagation on graphs
• Mon Mar 25. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
  • Assignment due: Subsampling and visualizing a graph.
  • New Assignment: mini-project proposals (final version)
• Wed Mar 27. Understanding spectral clustering techniques.
  • Assignment due: mini-project proposals (final version).

April and May

• Mon Apr 1. LDA-like models for text and graphs
• Wed Apr 3. To be decided
• Mon Apr 8. Speeding up LDA-like models: sampling and parallelization
• Wed Apr 10. Fast KNN and similarity joins 1.
• Mon Apr 15. Fast KNN and similarity joins 2.
  • Project progress report due
• Wed Apr 17. Scaling up decision tree learning
• Mon Apr 22. SGD for matrix factorization and online LDA
• Wed Apr 24. Guest lecture, Evangelos Papalexakis, on Scalable Tensor Methods.
• Mon Apr 29. Project reports.
• Wed May 1. Project reports.

May

• Fri May 3.
  • Project writeups due at 5:00pm. Submit a paper to Blackbook in PDF in the ICML 2013 format (up to 8pp double column), except, of course, do not submit it anonymously.