Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016

This is the syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016.

Notes:

• Homeworks, unless otherwise posted, will be due when the next HW comes out.
• Lecture notes and/or slides will be (re)posted around the time of the lectures.

note: this is under construction

September

• Thus Sep 1. Overview of course, cost of various operations, asymptotic analysis.
• Tues Sep 6. Review of probabilities, joint distributions and naive Bayes
  • HW1A out: streaming naive Bayes. draft Handout
• Thus Sep 8. Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.
• Tues Sep 13. Phrase Finding and Hadoop
  • HW1B out: naive Bayes training on Hadoop. draft Handout
  • Hadoop Overview
  • Phrase Finding
• Thus Sep 15. Implementing Phrase Finding and Large-Data Testing for Naive Bayes with Stream-and-Sort.
  • Lecture also discusses: map-reduce abstractions/dataflow
• Tues Sep 20, Thus Sep 22. Hadoop Workflow Languages and Rocchio and TFIDF
  • HW3 out: Using workflow languages.
  • Lecture also discusses: hadoop streaming, mrjob, cascading, pipes, scaling, hive, pig, spark, flink
• Tues Sep 27. Fast KNN and similarity joins
• Thus Sep 29. Scalable SGD and Hash Kernels
  • HW4 out: streaming logistic regression classifier PDF Handout
  • For 805 students: an initial project proposal is due via email to wcohen+805@gmail.com. You will get feedback on it from the instructors, and it will also be posted to the class - mainly for 605 students that are interested in collaborating, but also for general interest. Please be clear about your proposal. I'm expecting approximately one page. You should discuss what dataset you plan to use, what results you hope to obtain, what baseline technique you will build on and/or compare to. Also include a section saying if you have a partner; and if you are willing to work with/mentor one or more 605 students, and if so, how you anticipate them contributing to the project.

October

• Tues Oct 4. No class - Rosh Hashana.
• Thus Oct 6. Parallel Perceptrons 1.
• Tues Oct 11. Parallel Perceptrons 2.
• Thus Oct 13. More on parallel and streaming ML: Adaptive gradients, AllReduce, and Parameter Servers
  • William's note - revised, and discuss param servers more later on
• Tues Oct 18. Matrix Factorization and SGD
  • Also, some exam review tips (ppt
  • practice questions for midterm - v1. This document also references the relevant questions from two previous review sheets:
    • practice questions from final, 2014
    • practice questions for final, 2015
    • Some review tips - modified from last year's exam review session
  • For 805 students: the final project proposal is due.
• Thus Oct 20. midterm exam
• Tues Oct 25. Matrix Factorization and SGD
• Thus Oct 27. Randomized Algorithms 1

November

• Tues Nov 1. Randomized Algorithms 2
  • HW5 out: dSGD for modeling text ([1])
• Thus Nov 3. TBD
• Thues Nov 8. Scalable PageRank
• Tues Nov 10. SSL on Graphs
• Tues Nov 15. Sparse sampling and parallelization for LDA
  • HW6 out: approximate pagerank for sampling a graph ([2])
• Thus Nov 17. TBD
• Tues Nov 22. TBD
• Thus Nov 24. No class - happy Thanksgiving!
• Tues Nov 29. Speeding up LDA-like models: All-reduce and other tricks
  • HW7 out: LDA with a param server (PDF handout)

December

• Thus Dec 1. Graph models for large-scale ML
• Tues Dec 6. Review and project presentations (15 min each):
  • HW7 due
• Thus Dec 8. In-class exam.
• Tues Dec 15. Writeup for 10-805 projects are due (at 11:59pm).

Topics covered in previous years but not in 2015

• Scalable First-order logics
• Workflows in PIG
• Phrase Finding
• Scalable Similarity Joins
• Fast KNN and similarity joins
• Messages, records and workflows; Rocchio
• Scalable pagerank - The Schimmy Pattern
• Scalable spectral clustering techniques.