Pattern Recognition Class (2012) by Fred Hamprecht at Universität Heidelberg

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source: Universität Heidelberg     2012年11月22日
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

1.1 Applications of Pattern Recognition | 1 Introduction 25:46
1.2 k-Nearest Neighbors Classification | 1 Introduction 1:10:32
1.3 Probability Theory | 1 Introduction 54:41
1.4 Statistical Decision Theory | 1 Introduction 27:49
2.1 Pearson Correlation | 2 Correlation Measures, Gaussian Models 43:36
2.2 Alternative Correl. Measures | 2 Correl. Measures, Gaussian Models 37:02
2.3 Gaussian Graphical Models | 2 Correl. Measures, Gaussian Models 1:30:26
2.4 Discriminant Analysis | 2 Correl. Measures, Gaussian Models 14:18
3.1 Regularized LDA/QDA | 3 Dimensionality Reduction 7:59
3.2 Principal Component Analysis (PCA) | 3 Dimensionality Reduction 1:56:39
3.3 Bilinear Decompositions | 3 Dimensionality Reduction 1:07:32
4.1 History of Neural Networks | 4 Neural Networks 9:03
4.2 Perceptrons | 4 Neural Networks 49:39
4.3 Multilayer Perceptrons | 4 Neural Networks 45:00
4.4 The Projection Trick | 4 Neural Networks 48:42
4.5 Radial Basis Function Networks | 4 Neural Networks 8:51
5.1 Loss Functions | 5 Support Vector Machines 40:31
5.2 Linear Soft-Margin SVM | 5 Support Vector Machines 1:14:03
5.3 Nonlinear SVM | 5 Support Vector Machines 34:31
6.1 Kernels | 6 Kernels, Random Forest 1:27:31
6.2 One Class SVM | 6 Kernels, Random Forest 39:01
6.3 Random Forest | 6 Kernels, Random Forest 57:02
6.4 Random Forest Feature Importance | 6 Kernels, Random Forest 19:00
7.1 Least-Squares Regression | 7 Regression 35:49
7.2 Optimum Experimental Design | 7 Regression 27:49
7.3 Case Study: Functional MRI | 7 Regression 16:53
7.4 Case Study: CT | 7 Regression 18:53
7.5 Regularized Regression | 7 Regression 1:07:17
8.1 Gaussian Process Regression | 8 Gaussian Processes 38:06
8.2 GP Regression: Interpretation | 8 Gaussian Processes 1:05:26
8.3 Gaussian Stochastic Processes | 8 Gaussian Processes 12:54
8.4 Covariance Function | 8 Gaussian Processes 1:03:58
9.1 Kernel Density Estimation | 9 Unsupervised Learning 48:26
9.2 Cluster Analysis | 9 Unsupervised Learning 40:58
9.3 Expectation Maximization | 9 Unsupervised Learning 48:17
9.4 Gaussian Mixture Models | 9 Unsupervised Learning 43:28
10.1 Bayesian Networks | 10 Directed Graphical Models 59:53
10.2 Variable Elimination | 10 Directed Graphical Models 35:53
10.3 Message Passing | 10 Directed Graphical Models 39:53
10.4 State Space Models | 10 Directed Graphical Models 35:14
11.1 The Lagrangian Method | 11 Optimization 51:26
11.2 Constraint Qualifications | 11 Optimization 33:38
11.3 Linear Programming | 11 Optimization 50:37
11.4 The Simplex Algorithm | 11 Optimization 56:15
12.1 StructSVM | 12 Structured Learning 52:09
12.2 Cutting Planes | 12 Structured Learning 37:00