Difference between revisions of "Class meeting for 10-605 Randomized Algorithms"
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* [http://www.cs.cmu.edu/~wcohen/10-605/bloomfilter.py Python demo code for Bloom filter] | * [http://www.cs.cmu.edu/~wcohen/10-605/bloomfilter.py Python demo code for Bloom filter] | ||
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| + | === Quizzes === | ||
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| + | * [https://qna.cs.cmu.edu/#/pages/view/83 quiz for lecture 1] | ||
=== Optional Readings === | === Optional Readings === | ||
Revision as of 09:57, 3 November 2016
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:
Quizzes
Optional Readings
- Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering Deepak Ravichandran, Patrick Pantel, and Eduard Hovy
- Online Generation of Locality Sensitive Hash Signatures. Benjamin Van Durme and Ashwin Lall. ACL Short. 2010
- Sketch Algorithms for Estimating Point Queries in NLP. Amit Goyal, Hal Daume III, and Graham Cormode, EMNLP 2012]
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.
