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

Pattern Recognition Class (2011) by Fred Hamprecht at Universität Heidelberg

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source: Universität Heidelberg       2011年11月9日
A course on Pattern Recognition by Prof. Fred Hamprecht from the Physics department of the Heidelberg University. The course introduces modern machine learning methods with an eye to applications in image analysis.

Lecture 01, part 1  Introduction to pattern recognition and probability theory. This part introduces pattern recognition applications and the k-nearest neighbors classifier. 46:55
Lecture 01, part 2  50:25
Lecture 01, part 3 35:54
Lecture 02, part 1  38:09
Lecture 02, part 2 45:47
Lecture 02, part 3 42:29
Lecture 02, part 4 41:14
Lecture 03, part 1 42:05
Lecture 03, part 2 52:29
Lecture 03, part 3 58:00
Lecture 04, part 1 43:52
Lecture 04, part 2 58:20
Lecture 04, part 3 41:23
Lecture 04, part 4 23:08
Lecture 05, part 1 44:40
Lecture 05, part 2 34:44
Lecture 05, part 3 39:31
Lecture 05, part 4 42:14
Lecture 06, part 1 48:30
Lecture 06, part 2 1:00:54
Lecture 06, part 3 1:01:06
Lecture 06, part 4 6:47
Lecture 08, part 1 56:59
Lecture 08, part 2 52:50
Lecture 08, part 3 38:22
Lecture 09, part 1 37:52
Lecture 09, part 2 45:14
Lecture 09, part 3 24:46
Lecture 10, part 1 40:36
Lecture 10, part 2 36:42
Lecture 10, part 3 51:30
Lecture 10, part 4 45:00
Lecture 11, part 1 30:21
Lecture 11, part 2 46:59
Lecture 11, part 3 45:28
Lecture 11, part 4 45:28