Lecture 01 - The Learning Problem
1:21:27Lecture 02 - Is Learning Feasible?
1:16:48Lecture 03 -The Linear Model I
1:19:44Lecture 04 - Error and Noise
1:18:22Lecture 05 - Training Versus Testing
1:16:57Lecture 06 - Theory of Generalization
1:18:11Lecture 07 - The VC Dimension
1:13:31Lecture 08 - Bias-Variance Tradeoff
1:16:50Lecture 9: The Linear Model II (CS 156)
1:27:14Lecture 10: Neural Networks (CS 156)
1:25:16Lecture 11: Overfitting (CS 156)
1:19:49Lecture 12: Regularization (CS 156)
1:15:14Lecture 13: Validation (CS 156)
1:26:12Lecture 14: Support Vector Machines (CS 156)
1:14:16Lecture 15: Kernel Methods (CS 156)
1:18:19Lecture 16: Radial Basis Functions (CS 156)
1:22:08Lecture 17: Three Learning Principles (CS 156)
1:16:18Lecture 18: Epilogue (CS 156)
1:09:28Lecture 01 - The Learning Problem
Professor of Electrical Engineering and Computer Science
This inaugural lecture for Caltech's CS 156 introduces the core components of the learning problem, distinguishing between supervised, unsupervised, and reinforcement learning paradigms. It establishes foundational concepts for algorithmic inference.
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html
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Seminar notes & discussion
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