Machine Learning - Dynamical, Statistical, and Economic Perspectives

Machine Learning - Dynamical, Statistical, and Economic Perspectives

Society for Industrial and Applied Mathematics via YouTube Direct link

structure

11 of 17

11 of 17

structure

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Machine Learning - Dynamical, Statistical, and Economic Perspectives

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  1. 1 Intro
  2. 2 Realworld Machine Learning
  3. 3 Gradient Descent
  4. 4 Proof Technique
  5. 5 Lower Bounds
  6. 6 Unconstrained convex optimization
  7. 7 Lagrangian optimization
  8. 8 symplectic integration
  9. 9 conservative systems
  10. 10 geometry
  11. 11 structure
  12. 12 dissipative case
  13. 13 Open problems
  14. 14 Theorem
  15. 15 Dynamical Systems
  16. 16 Summary
  17. 17 QA

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