Difference between revisions of "Class meeting for 10-605 Randomized Algorithms"
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* Lecture 1 [http://www.cs.cmu.edu/~wcohen/10-605/randomized-1.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/randomized-1.pdf PDF]. | * Lecture 1 [http://www.cs.cmu.edu/~wcohen/10-605/randomized-1.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/randomized-1.pdf PDF]. | ||
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=== Quizzes === | === Quizzes === | ||
* [https://qna.cs.cmu.edu/#/pages/view/83 quiz for lecture 1] | * [https://qna.cs.cmu.edu/#/pages/view/83 quiz for lecture 1] | ||
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=== Sample Code === | === Sample Code === | ||
Revision as of 10:00, 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
- Lecture 1 Powerpoint, PDF.
Quizzes
Sample Code
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.
