Exploring Aa 19 20 Lecture 21
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- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Supervised learning, minimization (least squares), polynomial regression.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
In-Depth Information on Aa 19 20 Lecture 21
Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models. Fuzzy sets and clustering. Fuzzy c-means. Manifold learning. Second assignment. Hierarchical Clustering. Agglomerative and Divisive Clustering. Government Required Risk Disclaimer and Disclosure Statement CFTC RULE 4.41 – HYPOTHETICAL OR SIMULATED ...
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