Machine Learning Basics: A Speedrun - IPAM at UCLA

Machine Learning Basics: A Speedrun - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Matrix iterative optimization

20 of 23

20 of 23

Matrix iterative optimization

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Machine Learning Basics: A Speedrun - IPAM at UCLA

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  1. 1 Intro
  2. 2 Parameters
  3. 3 Inductive bias
  4. 4 Underfitting and overfitting
  5. 5 Considerations
  6. 6 Illustration
  7. 7 Optimal model complexity
  8. 8 Regularization terms
  9. 9 Crossvalidation
  10. 10 Data limitations
  11. 11 Linear regression
  12. 12 Ridge regression
  13. 13 Nonlinear regression
  14. 14 Kernel track
  15. 15 Kernel retrogression
  16. 16 Kernel as linear operator
  17. 17 Kernel trick
  18. 18 Energy contributions
  19. 19 Matrix factorization
  20. 20 Matrix iterative optimization
  21. 21 Preconditioning
  22. 22 Tradeoff
  23. 23 Nonlinearity

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