CS156-Machine Learning - California

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Lecture 01 - The Learning Problem
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01:21:28
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Lecture 02 - Is Learning Feasible?
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Lecture 03 -The Linear Model I
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01:19:44
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Lecture 04 - Error and Noise
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01:18:22
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Lecture 05 - Training Versus Testing
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Lecture 07 - The VC Dimension
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01:13:31
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Lecture 08 - Bias-Variance Tradeoff
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Lecture 09 - The Linear Model II
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01:27:14
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Lecture 10 - Neural Networks
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01:25:16
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Lecture 12 - Regularization
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01:15:14
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Lecture 14 - Support Vector Machines
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Lecture 15 - Kernel Methods
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01:18:19
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Lecture 18 - Epilogue
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01:09:28
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Lecture 16 - Radial Basis Functions
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Lecture 13 - Validation
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01:26:12
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Lecture 11 - Overfitting
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01:19:49

Lecture 01 - The Learning Problem

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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/co....urse/machine-learnin 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/

This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

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