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
• Tues Feb 28. Bloom Filters and Locality sensitive hashing 1.
  • Hadoop assignments due
  • New Assignment: memory-efficient SGD

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, ...)
  • Assignment due: 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).
  • 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.