Machine Learning Course - CS 156

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.