The Learning Problem and Regularization

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.