Difference between revisions of "Class meeting for 10-605 Scalable Testing"
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 Datase...") |
(→Slides) |
||
| (7 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 Fall 2015|schedule]] for the course [[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 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2015]]. |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/ | + | Testing a large classifier: |
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/a_nb-test.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/a_nb-test.pdf Slides in PDF] | ||
| + | |||
| + | Another sample algorithm: | ||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/b_phrases.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/b_phrases.pdf Slides in PDF] | ||
| + | |||
| + | Abstractions for map-reduce workflows: | ||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/c_abstractions.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/c_abstractions.pptx Slides in PDF] | ||
=== Readings for the Class === | === Readings for the Class === | ||
| − | * | + | * Required: [http://www.cs.cmu.edu/~wcohen/10-605/notes/scalable-nb-notes.pdf my notes on streaming and Naive Bayes] |
| + | * Optional: [http://hortonworks.com/hadoop-tutorial/how-to-process-data-with-apache-pig/ Pig] | ||
Latest revision as of 16:05, 10 September 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
Slides
Testing a large classifier:
Another sample algorithm:
Abstractions for map-reduce workflows:
Readings for the Class
- Required: my notes on streaming and Naive Bayes
- Optional: Pig
