Session Overview
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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. Image courtesy of Squiggle on Flickr. |
Session Activities
Lecture Videos
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Lecture 19: More Optimization and Clustering (00:49:43)
Lecture 19: More Optimization and Clustering
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About this Video
Topics covered: Knapsack problem, local and global optima, supervised and unsupervised machine learning, training error, clustering, linkage, feature vectors.
Resources
PDF)Check Yourself
What is machine learning?
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What is inductive inference?
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What is supervised learning?
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What is unsupervised learning used for?
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What is clustering?
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What is agglomerative clustering?
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