9/28/09-9/30/09

First lecture of the week by Dr. Fridsma. I'm finally getting a sense that this is all interconnected. His discussion had much focus on the syntactic web (the web as we know it; HTML based) and the semantic web (machine language-where information can be systematically read and understood by machines). We again heard mention of SNOMED, and its likely inevitability for medical ontology.
http://linkinghub.elsevier.com.ezproxy1.lib.asu.edu/retrieve/pii/S1386-5056(08)00091-9

Mr. Shuiwang's presentation was a bit clearer this week. Perhaps repeated exposure and linkage of material is finally working on me! His focus was primarily on supervised and unsupervised learning. Both are algorithm types used in machine learning. Unsupervised learning doesn't use labels and seeks to explain how data is organized. Clustering is a type of unsupervised learning, and assigns observations into clusters, all of which have something in common. K-means is a type of flat clustering. Supervised learning considers model relationships and is a technique for creating a function from training data. K-closest neighbor is of this type.

Back to reading! Lee B.