SML: Generalized Linear Models and Kernels

Content

• Kernel trick

  • Simple kernels

  • Kernel PCA

  • Mean Classifier

• Support Vectors

  • Support Vector Machine classification

  • Regression

  • Logistic regression

  • Novelty detection

• Gaussian Process Estimation

  • Regression

  • Classification

  • Heteroscedastic Regression

Supplementary material

Slides in PDF and Keynote. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it. There's also an optimization chapter from the Learning with Kernels book.

• Ramp loss consistency PDF

• Ranking and structured estimation PDF

• Invariances and convexity journal

• Ramp loss for structured estimation PDF

• Structured estimation (with margin rescaling) PDF

• Structured estimation (without margin rescaling) PDF

• Ben Taskar’s tutorial PPT

• SVM Tutorial (regression) PDF

• SVM Tutorial (classification) PDF

• Introductory chapter of Kernel book PDF

• Introductory chapter of structured estimation book PDF

• Kernel PCA journal

Videos

This is unedited video straight from a Lumix GF2 with a 20mm lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).