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