Lecture 01 - The Learning Problem Lecture 02 - Is Learning Feasible? Lecture 03 -The Linear Model I Lecture 04 - Error and Noise Lecture 05 - Training Versus Testing Lecture 06 - Theory of Generalization Lecture 07 - The VC Dimension Lecture 08 - Bias-Variance Tradeoff Lecture 09 - The Linear Model II Lecture 10 - Neural Networks Lecture 11 - Overfitting Lecture 12 - Regularization Lecture 13 - Validation Lecture 14 - Support Vector Machines Lecture 15 - Kernel Methods Lecture 16 - Radial Basis Functions Lecture 17 - Three Learning Principles Lecture 18 - Epilogue