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 16: Radial Basis Functions (CS 156)
This session introduces Radial Basis Functions (RBFs) as a key learning model, highlighting their connections to various machine learning techniques and architectures.
Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 16 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 15: Kernel Methods (CS 156)
Next · Lecture 17Lecture 17: Three Learning Principles (CS 156)