Applying MC Dropout to solve neural network overconfidence problem on wrong classification.
Extracting testcases from Decision trees based for solving Constraints to learn better through tree-search algorithms using SkLearn and XGBoost
Applying approximate Bayesian methods like Bootstrap on toy datasets for uncertainty quantification for a heteroscedastic dataset.
Deriving the posterior, MAP and posterior predictive for the Bernoulli distribution
