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Lecture 14 - Support Vector Machines
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Classroom Contents
Machine Learning Course
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- 1 Lecture 01 - The Learning Problem
- 2 Lecture 02 - Is Learning Feasible?
- 3 Lecture 03 -The Linear Model I
- 4 Lecture 04 - Error and Noise
- 5 Lecture 05 - Training Versus Testing
- 6 Lecture 06 - Theory of Generalization
- 7 Lecture 07 - The VC Dimension
- 8 Lecture 08 - Bias-Variance Tradeoff
- 9 Lecture 09 - The Linear Model II
- 10 Lecture 10 - Neural Networks
- 11 Lecture 11 - Overfitting
- 12 Lecture 12 - Regularization
- 13 Lecture 13 - Validation
- 14 Lecture 14 - Support Vector Machines
- 15 Lecture 15 - Kernel Methods
- 16 Lecture 16 - Radial Basis Functions
- 17 Lecture 17 - Three Learning Principles
- 18 Lecture 18 - Epilogue
