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Schedule for Spring 2011

The topics for this semester are Deep Belief Networks, Online Clustering, Manifold learning/feature selection. Deep Belief Networks Feb 9 (Ann Irvine) Read both Hinton’s Science article (http://www.cs.toronto.edu/~hinton/science.pdf) AND his Neural Computation article (http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf) OR Watch Hinton’s tutorial here: http://videolectures.net/mlss09uk_hinton_dbn/ (almost 90 minutes long) Feb 16 (Keith Kintzley) Part 2 of the Hinton tutorial Feb 23 (Ehsan Variani) […]

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Votes for topics (Spring 2011)

9 Online Clustering 8 Deep Belief Nets 8 Manifold Learning 7 Feature Selection 7 Graphical models inference 6 Kernels 5 Metric learning 5 non-parametric Bayes 1 Techniques for optimization 1 Decision theory 1 Decision trees 3 Reinforcement learning 4 Active learning 5 Hierarchical Bayesian methods 6 Semi-supervised learning 1 Transfer learning 3 Non-negative matrix factorization

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Selected Topics in Machine Learning

This page is for the Machine Learning reading group (CS 600.775 Selected Topics in Machine Learning). Meeting: Spring Semester Contact Mark Dredze (instructor) to be added to the mailing list. For general advice on presenting, see instructions on how to present in reading group. Also, see Jason Eisner’s advice on how to read a paper. […]

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