Class meeting for 10-605 Randomized

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This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.


Slides

  • TBD

Supplement:

Optional Readings

Key things to remember

  • The API for the randomized methods we studied: Bloom filters, LSH, CM sketches, and specifically, when you would use which technique.
  • The relationship between hash kernels and CM sketches.
  • What are the key tradeoffs associated with these methods, in terms of space/time efficiency and accuracy, and what sorts of errors are made by which algorithms (e.g., if they give over/under estimates, false positives/false negatives, etc).
  • What guarantees are possible, and how space grows as you require more accuracy.
  • Which algorithms allow one to combine sketches easily.