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Course: Pattern RecognitionWS 2009 - IUPR Courses
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WS 2009 - IUPR Courses
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Course: Pattern Recognition | pr.iupr.com Reviews
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WS 2009 - IUPR Courses
Introduction - Course: Pattern Recognition
http://pr.iupr.com/lectures/introduction
Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. Background, Python, MNIST data, and basic classification. Introduction to Pattern Recognition. Patrec - Introduction to Python - Sage.pdf. Patrec - The Seven Segments Example - Sage.pdf. Patrec - MNIST Data Worksheet - Sage.pdf. Skim ESLI Chapters 1 and 2. Due 4 Nov 2009).
Recent site activity - Course: Pattern Recognition
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Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. Feb 9, 2011, 10:34 AM. Thomas Breuel edited Lectures. Feb 9, 2011, 10:29 AM. Thomas Breuel deleted Lectures. Feb 9, 2011, 10:28 AM. Thomas Breuel edited Lectures. Feb 9, 2011, 10:28 AM. Thomas Breuel created Lectures. Feb 9, 2011, 10:28 AM. Thomas Breuel edited Parameter Estimation. Feb 9, 2011, 10:28 AM. Thomas Breuel created Parameter Estimation. Feb 9, 2011, 10:25...
Questions - Course: Pattern Recognition
http://pr.iupr.com/course/questions-1
Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. The following questions, or similar questions, may appear on the final exam. Use them as a study guide. The questions cover lectures, exercises, readings, and homework. Given histograms of the c.c.d., find the decision boundaries. What is the "empirical error rate"? How does it differ from the Bayes error rate? Describe the nearest neighbor classifier. What is a norm?
kNN Methods - Course: Pattern Recognition
http://pr.iupr.com/lectures/knn-methods
Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. Patrec - Nonparametric Classification - Sage.pdf. Patrec - kNN Methods - Sage.pdf. Patrec - kNN asymptotic error rate - Sage.pdf. Patrec - Parameter Estimation - Sage.pdf.
PCA, Statistical Foundations - Course: Pattern Recognition
http://pr.iupr.com/lectures/pca-statistical-foundations
Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. PCA, Statistical Foundations. Lecture 5 PCA, Statistical Foundations. Patrec - MNIST Nearest Neighbor Classification - Sage.pdf. Patrec - MNIST PCA - Sage.pdf. Patrec - MNIST PCA - Incremental - Sage.pdf. Patrec - MNIST Fourier - Sage.pdf.
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Course: Pattern Recognition
Classification with Normal Densities. Ellipses, Gaussians, Convariances. PCA, Statistical Foundations. Perceptrons and Logistic Regression. Hastie, Tibshirani, Friedman: Elements of Statistical Learning [ ESLI. Duda, Hart, Stork: Pattern Classification (library). English (lectures), English or German (exams, exercises). Mo, 08:15 - 09:45 48-462. Wed, 16:00 - 17:00 48-211 (attend even if there is no homework). All the Sage notebooks are now accessible as PDF files from this site. These algorithms are wide...
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