CS 156: Machine Learning Course (Spring 2012, Caltech) | Computer Science

CS 156: Machine Learning Course

CS 156: Machine Learning Course (Spring 2012, Caltech). 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.