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 02 - Is Learning Feasible?
Professor of Electrical Engineering and Computer Science
This lecture explores the fundamental question of generalization in machine learning: how to infer properties of an entire space from a limited sample. It introduces the relationship between in-sample and out-of-sample error, laying the groundwork for statistical learning theory.
Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 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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