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
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What is machine learning?
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"A scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data." From Wikipedia.
What is inductive inference?
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The observation of examples that represent incomplete information about some statistical phenomenon in order to recognize complex patterns and make intelligent decisions.
What is supervised learning?
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Learning in which a label is associated with each example in a training set.
What is unsupervised learning used for?
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Uncovering hidden regularities or detecting anomalies in data.
What is clustering?
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The process of organizing objects into groups whose members are similar in some way.
What is agglomerative clustering?
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Clustering that merges clusters iteratively.