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 05 - Training Versus Testing
Professor of Electrical Engineering and Computer Science
This lecture formally distinguishes training and testing phases in machine learning, exploring the mathematical conditions for model generalization. It addresses the core challenge of ensuring learned models perform effectively on unseen data.
Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 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
Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/
Lecture 04 - Error and Noise
Next · Lecture 6Lecture 06 - Theory of Generalization
Seminar notes & discussion
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