Courses:

Networks for Learning: Regression and Classification >> Content Detail

Calendar


	LEC #				TOPICS


	1				The Course at a Glance


	2				The Learning Problem in Perspective


	3				Regularized Solutions


	4				Reproducing Kernel Hilbert Spaces


	5				Classic Approximation Schemes


	6				Nonparametric Techniques and Regularization Theory


	7				Ridge Approximation Techniques


	8				Regularization Networks and Beyond


	9				Applications to Finance


	10				Introduction to Statistical Learning Theory


	11				Consistency of the Empirical Risk Minimization Principle


	12				VC-Dimension and VC-bounds


	13				VC Theory for Regression and Structural Risk Minimization


	14				Support Vector Machines for Classification


	15				Project Discussion


	16				Support Vector Machines for Regression


	17				Current Topics of Research I: Kernel Engineering


	18				Applications to Computer Vision and Computer Graphics


	19				Neuroscience I


	20				Neuroscience II


	21				Current Topics of Research II: Approximation Error and Approximation Theory


	22				Current Topics of Research III: Theory and Implementation of Support Vector Machines


	23				Current Topics of Research IV: Feature Selection with Support Vector Machines and Bioinformatics Applications


	24				Current Topics of Research V: Bagging and Boosting


	25				Selected Topic: Wavelets and Frames


	26				Project Presentation