Machine Learning Course

Overview

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.

Syllabus

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.