Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012

This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012.

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
  • Ron's slides in Powerpoint
  • Ron's slides in PDF
• 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 2.
  • Hadoop assignments due
  • New Assignment: memory-efficient SGD PDF writeup
  • New assignment: initial project proposals. PDF writeup
• Thus Mar 8. Guest Lecture: Joey Gonzales, CMU, GraphLab and Dynamic Asynchronous Computation PPT slides
• Tues Mar 13. no class - spring break.
• Thus Mar 15. no class - spring break.
• Tues Mar 20. 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
• Thus Mar 22. Semi-supervised learning via label propagation on graphs
• Tues Mar 27. 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)
• Thus Mar 29. Understanding spectral clustering techniques.
  • Assignment due: mini-project proposals (final version).

April

• Tues Apr 3. LDA-like models for text and graphs.
• Thus Apr 5. Tentative: Guest lecture by U Kang, CMU.
• Tues Apr 10. Speeding up LDA-like models: sampling and parallelization
• Thus Apr 12. Fast KNN and similarity joins 1.
• Tues Apr 17. Fast KNN and similarity joins 2.
• Thus Apr 19. no class - Carnival
• Tues Apr 24. Online LDA
• Thus Apr 26. TBD.

May

• Tues May 1. Project reports.
• Thus May 3. Project reports.