Kernel methods and Support Vector Machines
author:
Alexander J. Smola,
Australian National University - ANU
Description
The tutorial will introduce the main ideas of statistical learning
theory, support vector machines, and kernel feature spaces.
This includes a derivation of the support vector optimization
problem for classification and regression, the v-trick,
various kernels and an overview over applications of kernel methods.
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| Slides | |
| 0:00 | An Introduction to Machine Learning - L1: Basics and Probability Theory |
| 1:13 | Overview |
| 2:28 | L1 Introduction to Machine Learning |
| 2:57 | Outline - Data |
| 3:00 | Data |
| 7:40 | Optical Character Recognition |
| 9:09 | Reuters Database |
| 9:31 | Faces |
| 10:14 | More Faces |
| 10:23 | Microarray Data |
| 12:32 | Biological Sequences |
| 16:14 | Graphs |
| 16:22 | Missing Variables |
| 26:02 | Mini Summary |
| 28:08 | Outline - Data Analysis |
| 28:38 | What to do with data |
| 30:45 | Clustering |
| 30:53 | Principal Components |
| 31:04 | Linear Subspace |
| 31:29 | Classification |
| 32:55 | Regression (1) |
| 34:27 | Regression (2) |
| 36:49 | Annotating Strings |
| 37:03 | Annotating Audio |
| 37:48 | Novelty Detection |
| 39:49 | What Machine Learning is not |
| 41:57 | Eliza |
| 42:21 | How the brain doesn’t work |
| 42:32 | Mini Summary |
| 43:10 | Statistics and Probability Theory |
| 44:20 | Probability |
| 45:12 | Example |
| 45:26 | Multiple Variables |
| 45:58 | Independent Random Variables |
| 46:38 | Dependent Random Variables |
| 46:59 | Bayes Rule |
| 47:55 | Example |
| 47:58 | AIDS Test |
| 50:14 | Eye Witness |
| 50:16 | Improving Inference |
| 51:49 | Different Contexts |
| 52:48 | Mini Summary |
| 53:16 | Bayes Rule |
| 53:28 | Summary |
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Watch videos: (click on thumbnail to launch)
Part 1: Basics and Probability theory
0:54:04
|
Part 2: Instance Based Estimation
0:42:56
|
Part 3: Perceptron and Kernels
0:51:59
|
Part 4: Support Vector classification
0:46:32
|
Part 5: Novelty Detection and Regression
0:52:02
|
Part 6: Structured Estimation
1:04:29
|