Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2015"

From Cohen Courses
Jump to navigationJump to search
Line 22: Line 22:
 
== October ==
 
== October ==
  
* Tues Feb 17. [[Class meeting for 10-605 SGD and Hash Kernels|Scalable SGD and Hash Kernels]]
+
* Tues Oct 6. [[Class meeting for 10-605 SGD and Hash Kernels|Scalable SGD and Hash Kernels]]
** ''HW3: Naive Bayes with Hadoop MapReduce''.  PDF Handouts: [http://www.andrew.cmu.edu/user/amaurya/docs/10605/homework3.pdf  HW3].
+
* Thus Oct 8. [[Class meeting for 10-605 Randomized|Randomized Algorithms 1]]
** ''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.
+
* Tues Oct 13. [[Class meeting for 10-605 Randomized|Randomized Algorithms 2]]
* Thus Feb 19. [[Class meeting for 10-605 Randomized|Randomized Algorithms 1]]
+
* Thus Oct 15. [[Class meeting for 10-605 SGD for MF|Matrix Factorization and SGD]]
* Tues Feb 24. [[Class meeting for 10-605 Randomized|Randomized Algorithms 2]]
 
* Thus Feb 26. [[Class meeting for 10-605 SGD for MF|Matrix Factorization and SGD]]
 
  
 
== March  ==
 
== March  ==

Revision as of 17:22, 7 July 2015

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.

September

need to revise

October

March

  • Sun Mar 1.
    • HW3 due: Naive Bayes with Hadoop MapReduce
    • HW4: PDF wrteup
  • Tues Mar 3. student presentations
    • Adams Wei Yu (weiyu at andrew): fast PPR on Map-Reduce [1]
    • Jakub Pachocki: factorization machines (and hash kernels?) [2]
    • Wanli Ma (wanlim at andrew): coresets for k-segmentation of streams
  • 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.
    • 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) PDF and ppt.
  • Thus Mar 26. Guest lecture: D. Sculley, Google, TBA
  • Tues Mar 31. Sparse sampling and parallelization for LDA

April and May

  • Tues May 5.
    • For 10/11-805 students: project reports are due

Topics covered in previous years but not in 2015