Lecture 13 - Validation

Course Lessons

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