Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2015
From Cohen Courses
This is the syllabus for Machine Learning with Large Datasets 10-605 in Fall 2015.
Notes:
- The assignments posted are drafts based on the assignments from spring 2015, and will be modified over the course of the semester - some may be changed substantially.
- Lecture notes and/or slides will be (re)posted around the time of the lectures.
Contents
September
- Tues Sep 1. Overview of course, cost of various operations, asymptotic analysis.
- Thus Sep 3. Review of probabilities, joint distributions and naive Bayes
- Tues Sep 8. Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.
- Thus Sep 10. Messages, records and workflows; Phrase finding.
- Tues Sep 15. Hadoop and Map-Reduce
- Thus Sep 17. PIG and Other Workflow Systems for Hadoop
- Tues Sep 22. Rocchio and TFIDF
- Thus Sep 24. Fast KNN and similarity joins
- Tues Sep 29. Scalable SGD and Hash Kernels
- Thus Oct 1. TBA
October
- Tues Oct 6. Parallel Perceptrons 1.
- Thus Oct 8. Parallel Perceptrons 2.
- Tues Oct 13. Parameter servers and AllReduce
- Thus Oct 15. Matrix Factorization and SGD
- Tues Oct 20. TBA
- Thus Oct 22. midterm exam
- Tues Oct 27. Randomized Algorithms 1
- Thus Oct 29. Randomized Algorithms 2
November
- Tues Nov 3. Scalable PageRank
- Thus Nov 5. Subsampling a graph with RWR
- Tues Nov 10. SSL on Graphs
- Thus Nov 12. Graph models for large-scale ML
- Tues Nov 17. Sparse sampling and parallelization for LDA
- Thus Nov 19. Speeding up LDA-like models: All-reduce and other tricks
- Tues Nov 24. TBA
- Thus Nov 26. Happy Thanksgiving!
December
- Tues Dec 1. First-order logics
- Thus Dec 3. Scalable First-order logics
- Tues Dec 8. Scalable spectral clustering techniques.
- Thus Dec 10. In-class exam.
