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Covers classical force fields, molecular dynamics simulations, and supramolecular properties, including intramolecular and intermolecular interactions.
Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.
