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Lecture 19: More Optimization and Clustering

This video was recorded at 6.00SC Introduction to Computer Science and Programming, Spring 2011 . This lecture continues to discuss optimization in the context of the knapsack problem, and talks about the difference between greedy approaches and optimal approaches. It then moves on to discuss supervised and unsupervised machine learning optimization problems. Most of the time is spent on clustering. Topics covered: Knapsack problem, local and global optima, supervised and unsupervised machine learning, training error, clustering, linkage, feature vectors.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: John Guttag, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, MIT
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States