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 9: The Linear Model II (CS 156)
This lecture delves into advanced linear models, covering logistic regression, the principles of maximum likelihood estimation, and the iterative optimization process of gradient descent.
The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent. Lecture 9 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 08 - Bias-Variance Tradeoff
Next · Lecture 10Lecture 10: Neural Networks (CS 156)