Educational framework for Black Box optimization methods design
summary
Shows concrete how-to: uses Scipy.optimize and Scikit-learn notebooks, builds small tutorials on alopi for automatic differentiation, and applies them to compute Black‑Scholes Greeks that match analytic formulas. Implements logistic regression from likelihood, compares derivative‑free Nelder‑Mead and hybrid restarts, outlines householder coordinate transforms, and provides GitHub notebooks and test cases so results and runtimes can be inspected rather than left as vague examples.
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chapters (7)
- 0:00Educational framework intro
- 6:11Tutorials teach ML methods
- 15:17Automatic differentiation in finance
- 26:56Logistic regression tutorial
- 59:29Scikit-learn logistic regression
- 1:00:54Future tutorial plans
- 1:14:34Q&A
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