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  • Lesson.No : 13
  • 01:26:12

  • Lecture 13 - Validation

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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