Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Covers the efficient computation of heat capacity in metal organic frameworks using quantum classical methods and explores advanced PIMD techniques beyond benchmarks.
Explores Monte-Carlo methods for reinforcement learning, comparing them with TD-methods and emphasizing the efficiency of TD methods in propagating information.
Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
