Exploring Aa 17 18 Lecture 3
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- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Key Highlights – IFRS
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
- Bayesian Decision theory. Maximum a posteriori estimation. Decisions and costs.
In-Depth Information on Aa 17 18 Lecture 3
Overfitting and regularization with polynomial regression. Select models: Train, validate, test. Introduction. Introduction to clustering. K-means and k-medoids. Expectation maximization. Multiclass classification. Bootstrapping. Bias-variance decomposition and tradeoff.
Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second ...
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