Exploring Aa 18 19 Lecture 20
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- In this
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering.
- Introduction.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
In-Depth Information on Aa 18 19 Lecture 20
Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second ... Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation. Exercise Notebook: http://www.ds100.org/sp20/resources/assets/
Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
In summary, understanding Aa 18 19 Lecture 20 gives us a better perspective.