Plan expensive experiments using probabilistic surrogates and acquisition strategies.
Move from the motivation for Bayesian optimization to surrogate-based experimental design and automated performance tuning. Learners reason about uncertainty, acquisition choices, noisy objectives, and practical constraints such as evaluation cost and parameter domains. The course emphasizes reproducible comparisons with random search and careful reporting of the search budget; original notes support the English-audio videos without copying transcripts.
Explain how a probabilistic surrogate and an acquisition rule guide sequential objective evaluations. Choose search spaces and validation protocols that reflect mixed, bounded, noisy, or expensive parameters. Compare Bayesian optimization with random or grid search under a matched budget and report uncertainty and reproducibility details.
Familiarity with supervised machine learning, hyperparameters, and basic probability is recommended. Comfort interpreting plots and validation metrics helps; prior Gaussian-process implementation experience is not required.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
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