550:640 Machine Learning
Spring 2009
Whitehead 304, 3:00-4:15pm
Laurent Younes
Department of Applied Mathematics and Statistics
Center for Imaging Science
The Johns Hopkins University
3400 N Charles Street
21218 Baltimore MD
Clark 324C
Email: (replace * with dots): laurent*younes at jhu*edu
Office hours: By appointment.
Textbook: The class follows the handouts available below.
Content of the course (probably a superset):
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Introduction to density estimation. |
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Linear methods for regression and classification (regression, ridge regression, lasso, linear support vector machines; logistic regression, linear discriminant analysis) |
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The notion of feature space; an introduction to kernels; kernel SVM |
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Nearest neighbor methods |
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Introduction to model complexity |
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Boosting |
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Trees and random forests |
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Unsupervised methods: (kernel) PCA and variants, multidimensional scaling, local linear embedding, isomap, independent component analysis |
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Clustering |
Projects (password protected):
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project 1 (additional file project1.dat) |
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project 2 (additional file project2.zip) |
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project 3 (additional file project3.zip) |
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project 5 (additional file project5.zip) |
Links to a few packages:
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A matlab SVM (A. Schwaighofer) |
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SVM-light |
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Tom Briggs's wrapper to SVM-light, with precompiled libraries here |
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An R package containing SVM's |