Exploring Aa 19 20 Lecture 13
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- Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment.
- Introduction.
- Hierarchical Clustering. Agglomerative and Divisive Clustering.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Introduction to unsupervised learning. Data visualization and feature selection.
In-Depth Information on Aa 19 20 Lecture 13
Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs. Irrepressible or Needless/Slavery or States' Rights? What Caused the Civil War? In this DeVane Perceptron and Multilayer Perceptron.
Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
That wraps up our extensive overview of Aa 19 20 Lecture 13.