Problem Solving with Large Clusters
Izhak Shafran & Richard Sproat

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News and Assignments

Course Information

This course aims to provide theoretical foundations and practical experience in distributed algorithms. The techniques covered in this course have wide application. Examples will be drawn from speech and language processing, machine learning, optimization, and graph theory. The course will be a combination of: Prerequisites: A graduate level course on machine learning or probability and statistics. Students should be comfortable coding in at least one programming language.

Lectures and Readings

Textbook and Other Useful Resources

Note: For recently developed techniques, we will rely on selected papers, which will be provided in required readings.


MeetingsTue 930 - 1230 hrs
VenueWCC 403
Office hoursBy appointment (request by email)


Relevant Software Tools & Other Resources

This page is maintained by Zak Shafran. Last updated on Feb 4, 2010.