B659: Kernel-based Methods in Machine Learning
To study the theory and practice of constructing and applying kernel-based learning algorithms. Mathematical foundations, learning methodology, linear machines, dual representations, support vector classification and regression, kernel density estimation, kernel PCA, structured-output learning, applications in text mining, bioinformatics, and computational social sciences.
Semester(s) Offered: Fall
Class time: Wednesday : 1pm-3:30pm
Instructor: Predrag Radivojac - email@example.com
Other Contact(s): Predrag Radivojac - firstname.lastname@example.org
Day(s) per week offered: One.
Books used: An introduction to support vector machines and other kernel-based learning methods - by N. Cristianini and J. Shawe-Taylor, Cambridge University Press 2000.
Comments: Supplementary material will be provided for several topics.