Difference between revisions of "Class meeting for 10-605 Randomized"
From Cohen Courses
Jump to navigationJump to search (Created page with "This is one of the class meetings on the schedule for the course Machine Learning with Large Data...") |
|||
| (11 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. |
=== Slides === | === Slides === | ||
| − | * | + | * TBD |
| − | + | ||
| + | Supplement: | ||
| + | |||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/bloomfilter.py Python demo code for Bloom filter] | ||
=== Optional Readings === | === Optional Readings === | ||
| + | * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering] Deepak Ravichandran, Patrick Pantel, and Eduard Hovy | ||
* [http://www.cs.jhu.edu/~vandurme/papers/VanDurmeLallACL10.pdf Online Generation of Locality Sensitive Hash Signatures]. Benjamin Van Durme and Ashwin Lall. ACL Short. 2010 | * [http://www.cs.jhu.edu/~vandurme/papers/VanDurmeLallACL10.pdf Online Generation of Locality Sensitive Hash Signatures]. Benjamin Van Durme and Ashwin Lall. ACL Short. 2010 | ||
* [http://www.umiacs.umd.edu/~amit/Papers/goyalPointQueryEMNLP12.pdf Sketch Algorithms for Estimating Point Queries in NLP.] Amit Goyal, Hal Daume III, and Graham Cormode, EMNLP 2012] | * [http://www.umiacs.umd.edu/~amit/Papers/goyalPointQueryEMNLP12.pdf 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. | ||
Latest revision as of 16:28, 11 August 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:
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.
