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CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference. All lecture slides will be available as .ppt, .ps, & .htm at www.cs.toronto.edu/~hinton Many of the figures are provided by Chris Bishop

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CIAR Summer School Tutorial Lecture 1a: Mixtures of Gaussians, EM, and Variational Free Energy

CIAR Summer School Tutorial Lecture 1a: Mixtures of Gaussians, EM, and Variational Free Energy. Geoffrey Hinton. Stochastic generative model using directed acyclic graph (e.g. Bayes Net) Generation from model is easy Inference can be hard Learning is easy after inference.

548 views • 39 slides

Kentucky Common Core Standards Professional Development Resources

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CSC321: Neural Networks Lecture 12: Clustering

CSC321: Neural Networks Lecture 12: Clustering. Geoffrey Hinton. Clustering. We assume that the data was generated from a number of different classes. The aim is to cluster data from the same class together. How do we decide the number of classes?

358 views • 21 slides

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