Exploring Aa 17 18 Lecture 17
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- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation.
- We introduce moment generating functions (MGFs), which have many uses in probability. We also discuss Laplace's rule of ...
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Fuzzy sets and clustering. Fuzzy c-means. Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Second ...
- Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
In-Depth Information on Aa 17 18 Lecture 17
Introduction to clustering. K-means and k-medoids. Expectation maximization. Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. MIT 8.04 Quantum Physics I, Spring 2013 View the complete course: http://ocw.mit.edu/8-04S13 Instructor: Allan Adams In this ... Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
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