Exploring Aa 17 18 Lecture 18
Exploring Aa 17 18 Lecture 18 reveals several interesting facts.
- Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second ...
- Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
- Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
In-Depth Information on Aa 17 18 Lecture 18
Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation. Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms. Introduction to clustering. K-means and k-medoids. Expectation maximization.
Introduction.
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