Bayesian Kernel Methods

Introduction to Machine Learning - 10-701/15-781

Slides in Keynote and PDF.

Content

Gaussians
- Estimating a Normal Distribution
- Conditioning
- Principal Component Analysis
Gaussian Processes
- Correlation and inference
- Gaussian Process Regression
- Gaussian Process Classification

Girosi, 1998; An equivalence between sparse approximation and Support Vector Machines  ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1606.pdf
Smola, Schoelkopf and Mueller, 1998; The connection between regularization operators and Support Vector Kernels  http://alex.smola.org/teaching/berkeley2012/slides/Smola1998connection.pdf
Schoelkopf, Smola, Mueller, 1998; Nonlinear Component Analysis as Kernel Eigenvalue Problem  http://www.mitpressjournals.org/doi/abs/10.1162/089976698300017467
Scholkopf, Smola, Herbrich, 2001; A Generalized Representer Theorem  http://alex.smola.org/papers/2001/SchHerSmo01.pdf
Teo, Globerson, Roweis, Smola, 2008; Convex Learning with Invariances  http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2007_1047.pdf
Rasmussen, 2006; Gaussian Processes for Machine Learning  http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.3414