Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015"
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* Sun Mar 1. | * Sun Mar 1. | ||
** '''HW3 due: Naive Bayes with Hadoop MapReduce''' | ** '''HW3 due: Naive Bayes with Hadoop MapReduce''' | ||
+ | ** HW4: [http://www.andrew.cmu.edu/user/amaurya/docs/10605/homework4.pdf PDF wrteup] | ||
* Tues Mar 3. ''student presentations'' | * Tues Mar 3. ''student presentations'' | ||
** Adams Wei Yu (weiyu at andrew): fast PPR on Map-Reduce [http://www.cs.cmu.edu/~wcohen/10-605/2015-guest-lecture/ppr_mapreduce.pdf] | ** Adams Wei Yu (weiyu at andrew): fast PPR on Map-Reduce [http://www.cs.cmu.edu/~wcohen/10-605/2015-guest-lecture/ppr_mapreduce.pdf] | ||
Line 62: | Line 63: | ||
* Tues Mar 24. | * Tues Mar 24. | ||
** Student presentation: Rohan Ramanath, Bayesian Optimization | ** Student presentation: Rohan Ramanath, Bayesian Optimization | ||
− | ** Guest lecture: Dai Wei, CMU, Parameter servers. ('''Note''': This will be very relevant for one of the later HWs). | + | ** Guest lecture: Dai Wei, CMU, Parameter servers. ('''Note''': This will be very relevant for one of the later HWs) [https://dl.dropboxusercontent.com/u/65353654/daiwei01_release.pdf PDF] and [https://dl.dropboxusercontent.com/u/65353654/daiwei01_release.pptx ppt]. |
* Thus Mar 26. Guest lecture: D. Sculley, Google, TBA | * Thus Mar 26. Guest lecture: D. Sculley, Google, TBA | ||
* Tues Mar 31. [[Class meeting for 10-605 LDA 1|Sparse sampling and parallelization for LDA]] | * Tues Mar 31. [[Class meeting for 10-605 LDA 1|Sparse sampling and parallelization for LDA]] | ||
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** '''HW6 due: Subsampling and visualizing a graph.''' | ** '''HW6 due: Subsampling and visualizing a graph.''' | ||
** ''HW7: Matrix Factorization in Spark'' [http://www.andrew.cmu.edu/user/amaurya/docs/10605/homework7.pdf HW7 PDF Handout] [http://www.cs.cmu.edu/~yipeiw/TA605/hw7/eval2.pyc Evaluation Script][http://www.cs.cmu.edu/~yipeiw/TA605/hw7/eval_acc.py Validation Script] | ** ''HW7: Matrix Factorization in Spark'' [http://www.andrew.cmu.edu/user/amaurya/docs/10605/homework7.pdf HW7 PDF Handout] [http://www.cs.cmu.edu/~yipeiw/TA605/hw7/eval2.pyc Evaluation Script][http://www.cs.cmu.edu/~yipeiw/TA605/hw7/eval_acc.py Validation Script] | ||
− | * Thus Apr 2. [[Class meeting for 10-605 2013 LDA 2|Speeding up LDA-like models: All-reduce and | + | * Thus Apr 2. [[Class meeting for 10-605 2013 LDA 2|Speeding up LDA-like models: All-reduce and other tricks]] |
* Tues Apr 7. Guest lecture - Alex Beutel, SGD for Tensors | * Tues Apr 7. Guest lecture - Alex Beutel, SGD for Tensors | ||
** [http://www.cs.cmu.edu/~wcohen/10-605/2015-guest-lecture/beutel.pptx Alex's slides] | ** [http://www.cs.cmu.edu/~wcohen/10-605/2015-guest-lecture/beutel.pptx Alex's slides] | ||
Line 83: | Line 84: | ||
* Thus Apr 16. ''no class : carnival'' | * Thus Apr 16. ''no class : carnival'' | ||
** '''HW7 due''' | ** '''HW7 due''' | ||
− | ** ''HW8: [ | + | ** ''HW8: [http://bit.ly/605_hw8_ps Matrix factorization on parameter server] |
* Tues Apr 21. [[Class meeting for 10-605 GraphLab|Graph models for large-scale ML]] | * Tues Apr 21. [[Class meeting for 10-605 GraphLab|Graph models for large-scale ML]] | ||
* Thus Apr 23. ''Presentation for 10/11-805 projects'' | * Thus Apr 23. ''Presentation for 10/11-805 projects'' | ||
* Tues Apr 28. Exam review session. | * Tues Apr 28. Exam review session. | ||
** '''HW8: due''' | ** '''HW8: due''' | ||
− | ** [http:// | + | ** [http://www.cs.cmu.edu/~wcohen/10-605/practice-questions/s2014-final.pdf PDF practice questions from 2014] |
− | ** [http://www.cs.cmu.edu/~wcohen/10-605/exam-review.pptx Review session slides] | + | ** [http://www.cs.cmu.edu/~wcohen/10-605/practice-questions/s2015-final.pdf PDF practice questions for 2015] |
+ | ** [http://www.cs.cmu.edu/~wcohen/10-605/exam-review.pptx Review session slides], [http://www.cs.cmu.edu/~wcohen/10-605/exam-review.pdf PDF] | ||
* Thus Apr 30. In-class exam. | * Thus Apr 30. In-class exam. | ||
Latest revision as of 14:50, 14 October 2015
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015.
Notes:
- The assignments posted are drafts based on the assignments from 2014, 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
January
- Tues Jan 13. Overview of course, cost of various operations, asymptotic analysis.
- Thus Jan 15. Review of probabilities, joint distributions and naive Bayes
- HW1A: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- Tues Jan 20. Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large feature sets.
- HW1B: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
- For 10/11-805 students: a one-paragraph summary of a recent research result you'd like to present is due. If you're planning/hoping to transfer from 605, but haven't yet transferred, then also submit this assignment. Email to wcohen+805 AT gmail.com with the subject "Presentation" and include, in addition to your summary:
- Your name and andrew id
- A link to the paper
- Your best guess as to what lectures should precede the presentation
- Due by 11:59:59pm EST Tuesday.
- Thus Jan 22. Messages, records and workflows; Phrase finding.
- Tues Jan 27. Hadoop and Map-Reduce
- Thus Jan 29. PIG and Other Workflow Systems for Hadoop
- HW1A and HW1B due.
- HW2: phrase finding with stream-and-sort. PDF Handout Stopword List
February
- Tues Feb 3. Rocchio and TFIDF
- Thus Feb 5. Fast KNN and similarity joins
- Tues Feb 10. Parallel Perceptrons 1.
- Thus Feb 12. Parallel Perceptrons 2.
- HW2 due: phrase finding with stream-and-sort
- Tues Feb 17. Scalable SGD and Hash Kernels
- HW3: Naive Bayes with Hadoop MapReduce. PDF Handouts: HW3.
- For 10/11-805 students: initial draft of project proposal is due. I will give you feedback on this, so 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.
- Thus Feb 19. Randomized Algorithms 1
- Tues Feb 24. Randomized Algorithms 2
- Thus Feb 26. Matrix Factorization and SGD
March
- Sun Mar 1.
- HW3 due: Naive Bayes with Hadoop MapReduce
- HW4: PDF wrteup
- Tues Mar 3. student presentations
- Thus Mar 5. student presentations
- Quiz: [3]
- Matt Gardner (mg1 at cs): Large-scale extensions of the path ranking algorithm [4]
- Jesse Dodge (jessed at andrew): large-scale lasso regularization [5]
- Ishan Misra (imisra at andrew): LSH for object detection [6]
- HW5: memory-efficient SGD PDF handout
- For 10/11-805 students: project proposal is due. This must contain a complete description of the data you will use.
- Sat Mar 7 (extended from Friday):
- HW4 due: Phrase-finding with Hadoop
- Tues Mar 10. no class - spring break.
- Thus Mar 12. no class - spring break.
- Tues Mar 17. Scalable PageRank PDF handout
- Thus Mar 19. Subsampling a graph with RWR
- HW5 due: memory-efficient SGD
- HW6: Subsampling and visualizing a graph. PDF handout
- Tues Mar 24.
- Thus Mar 26. Guest lecture: D. Sculley, Google, TBA
- Tues Mar 31. Sparse sampling and parallelization for LDA
April and May
- Wed April 1
- HW6 due: Subsampling and visualizing a graph.
- HW7: Matrix Factorization in Spark HW7 PDF Handout Evaluation ScriptValidation Script
- Thus Apr 2. Speeding up LDA-like models: All-reduce and other tricks
- Tues Apr 7. Guest lecture - Alex Beutel, SGD for Tensors
- Thus Apr 9. Guest lecture - Alex Smola, Scalable parameter servers
- If you don't like the MediaTech one, a Youtube video on is also available for Alex's talk.
- Mon Apr 13. Informal update due for students working on project teams due.
- Each student working on a project should send to wcohen+805@gmail.com an update, between 1/2 page and 1 page long, saying what concrete tasks you've accomplished to date, how these tasks are part of the overall project (if you're not the only member), and what you plan to do between 4/13 and the presentation on 4/23.
- Additionally, each project lead (i.e., each 805 student that has any 10-605 student working with them) should add a list of who's working on their project, and one line indicating if they're making good progress so far.
- Tues Apr 14. SSL on Graphs
- Thus Apr 16. no class : carnival
- HW7 due
- HW8: Matrix factorization on parameter server
- Tues Apr 21. Graph models for large-scale ML
- Thus Apr 23. Presentation for 10/11-805 projects
- Tues Apr 28. Exam review session.
- Thus Apr 30. In-class exam.
- Tues May 5.
- For 10/11-805 students: project reports are due