12.2 Cutting Planes | 12 Structured Learning | Pattern Recognition Class 2012

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/Teaching/pr/ Playlist with all videos: http://goo.gl/gmOI6 Contents of this recording: cutting planes optimization 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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