The elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman, Springer.
550:640 Machine Learning
Spring 2008
Shaffer 302, 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.
Interesting reference:
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The elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman, Springer. |
Content of the course:
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Introduction to density estimation. |
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Linear methods for regression and classification (regression, ridge regression, 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:
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Project 1 (with data file: project1.dat) |
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Project 2 (with data files: project2_1.dat, project2_2.dat, project2_3.dat) |
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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 |