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12 kNearest Neighbors Classification 1 Introduction Pattern Recognition Class 2012
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=So0W65mUhDM
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. • It took place at the HCI / University of Heidelberg during the summer term of 2012. • Website: http://hci.iwr.uni-heidelberg.de/MIP/... • Playlist with all videos: http://goo.gl/gmOI6 • Contents of this recording: • 00:10:05 - Voronoi Tessellation • 00:09:05 - 1-Nearest Neighbor Classifier • 00:16:35 - decision boundary • 00:21:05 - k-Nearest-Neighbors • 00:36:10 - efficiency of k-NN • 00:37:40 - complexity of k-NN • 00:46:28 - kD-tree • 00:55:24 - spill trees • 01:02:30 - locality sensitive hashing (LSH) • Syllabus: • 1. Introduction • 1.1 Applications of Pattern Recognition • 1.2 k-Nearest Neighbors Classification • 1.3 Probability Theory • 1.4 Statistical Decision Theory • 2. Correlation Measures, Gaussian Models • 2.1 Pearson Correlation • 2.2 Alternative Correlation Measures • 2.3 Gaussian Graphical Models • 2.4 Discriminant Analysis • 3. Dimensionality Reduction • 3.1 Regularized LDA/QDA • 3.2 Principal Component Analysis (PCA) • 3.3 Bilinear Decompositions • 4. Neural Networks • 4.1 History of Neural Networks • 4.2 Perceptrons • 4.3 Multilayer Perceptrons • 4.4 The Projection Trick • 4.5 Radial Basis Function Networks • 5. Support Vector Machines • 5.1 Loss Functions • 5.2 Linear Soft-Margin SVM • 5.3 Nonlinear SVM • 6. Kernels, Random Forest • 6.1 Kernels • 6.2 One-Class SVM • 6.3 Random Forest • 6.4 Random Forest Feature Importance • 7. Regression • 7.1 Least-Squares Regression • 7.2 Optimum Experimental Design • 7.3 Case Study: Functional MRI • 7.4 Case Study: Computer Tomography • 7.5 Regularized Regression • 8. Gaussian Processes • 8.1 Gaussian Process Regression • 8.2 GP Regression: Interpretation • 8.3 Gaussian Stochastic Processes • 8.4 Covariance Function • 9. Unsupervised Learning • 9.1 Kernel Density Estimation • 9.2 Cluster Analysis • 9.3 Expectation Maximization • 9.4 Gaussian Mixture Models • 10. Directed Graphical Models • 10.1 Bayesian Networks • 10.2 Variable Elimination • 10.3 Message Passing • 10.4 State Space Models • 11. Optimization • 11.1 The Lagrangian Method • 11.2 Constraint Qualifications • 11.3 Linear Programming • 11.4 The Simplex Algorithm • 12. Structured Learning • 12.1 structSVM • 12.2 Cutting Planes
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