CS 156 - Machine Learning Course

Caltech - CS 156: Machine Learning Course (Spring 2012). This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which we do not have full mathematical specification (and that covers a lot of systems). The course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

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

References
Learning From Data
The Lectures. Homeworks. Textbook. Forum. Free, introductory Machine Learning course. Taught by Caltech Professor Yaser Abu-Mostafa.