Explore the fundamental concepts of machine learning and regularization in this comprehensive lecture by Prof. Tomaso Poggio. Delve into key topics such as training sets, expected error, generalization error, consistency, learning algorithms, stability, well-posed problems, and empirical risk minimization. Gain insights into the importance of regularization and its application in Reproducing Kernel Hilbert Spaces. Enhance your understanding of the learning problem and its solutions through this in-depth presentation.
Overview
Syllabus
Introduction
Training set
Key Property
Expected Error
Generalization Error
Consistency
Learning Algorithm
Stability
Wellposed Problems
Empirical Risk minimization
Example
Regularization
Reproducing Kernel Burst Spaces
Taught by
MITCBMM
