Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015

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This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015.


  • 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.




  • 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