## SML: Generalized Linear Models and KernelsSTATISTICS 241B,
COMPUTER SCIENCE 281B
## ContentKernel 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 materialSlides 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 Taskarâ€™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
## VideosThis 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). |