Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014

This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014.

Notes:

• The assignments are from 2013, and will be modified over the course of the semester - some may be changed substantially.
• Lecture notes will be posted around the time of the lectures.

January

• Mon Jan 13. Overview of course, cost of various operations, asymptotic analysis.
• Wed Jan 15. Review of probabilities, joint distributions and naive Bayes
• Mon Jan 20. No class - Martin Luther King Day.
• Wed Jan 22. 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 27. Messages and records 1; Phrase finding.
  • Assignment due: streaming Naive Bayes 1 (with feature counts in memory).
• Wed Jan 29. Phrase Finding and Rocchio
  • New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
• Thursday Jan 30. Scheduled down-time for the wiki host. (Obviously, it's up again now!)

February

• Mon Feb 3. Rocchio and Parallel Perceptrons
• Wed Feb 5. Perceptrons/Map-reduce and Hadoop.
  • 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
• Mon Feb 10. Parallel Perceptrons.
• Wed Feb 12. Guest lecture: Matt Hurst, Microsoft/Bing: Local Search at Bing. One-on-one meetings with Matt can be scheduled for Thursday 12/13 between 9-12 in Gates-Hillman 6501, afternoon meetings 12:30-1:30pm in Gates-Hillman 6002.
• Mon Feb 17. Scalable SGD and Hash Kernels
  • Assignment due: phrase finding with stream-and-sort
  • New Assignments: Naive Bayes with Streaming Hadoop, Naive Bayes with Streaming Hadoop & Phrase-finding with Hadoop. PDF Handout (draft, from 2013)
• Wed Feb 19. Matrix Factorization and SGD
• Fri Feb 21.
  • Streaming run on Hadoop of Naive Bayes due
• Mon Feb 24. Background on randomized algorithms; Graph computations 1.
  • Non-streaming run on Hadoop of Naive Bayes due
• Wed Feb 26. Graphs computations 2

March

• Mon Mar 3. Guest Lecture: Garth Gibson, topic TBA
• Wed Mar 5. Guest lecture: Alex Beutel, SGD on Hadoop
• Fri Mar 7.
  • Hadoop assignment (phrase-finding) due
  • New Assignment: memory-efficient SGD PDF writeup (draft)
• Mon Mar 10. no class - spring break.
• Wed Mar 12. no class - spring break.
• Mon Mar 17. Subsampling a graph with RWR
• Wed Mar 19. Semi-supervised learning via label propagation on graphs
  • Assignment due: memory-efficient SGD
  • New Assignment: Subsampling and visualizing a graph. PDF writeup
• Mon Mar 24. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
• Wed Mar 26. Understanding spectral clustering techniques.
• Mon Mar 31. Sparse sampling and parallelization for LDA

April and May

• Wed Apr 2. 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 7. Fast KNN and similarity joins 1.
• Wed Apr 9. Fast KNN and similarity joins 2.
• Mon Apr 14. Scaling up decision tree learning
• Wed Apr 16. Gradient boosting with trees
  • Assignment due: K-Means on MapReduce.
  • New Assignment: Multi-class image classification or scalable classification using a linear classifier.
    • PDF writeup of image-classification assignment
    • PDF writeup of scalable classification
• Mon Apr 21. TBD
• Wed Apr 23. TBD
• Mon Apr 28. TBD
  • Assignment due: Multi-class image classification or scalable classification.
• Wed Apr 30. In-class exam.