How placing a prior on linear regression weights gives a full posterior distribution over predictions - with closed-form solutions, predictive uncertainty, and connections to ridge regression.
How to place priors on neural network weights and approximate the posterior with variational inference or Monte Carlo dropout - with production trade-offs.
How Bayesian Optimisation uses Gaussian Processes and acquisition functions to find near-optimal hyperparameters in far fewer evaluations than grid or random search - with full Python implementation using BoTorch and Optuna.
Conformal prediction constructs prediction sets with provable finite-sample coverage guarantees under only the exchangeability assumption - no distributional assumptions required. Complete Python implementation for classification and regression.
Gaussian processes provide a full distribution over functions with principled uncertainty estimates - how they work, kernel engineering, and when to use them over neural networks.
Master Bayesian machine learning - from prior/posterior reasoning through Gaussian processes, Bayesian neural networks, and uncertainty quantification to conformal prediction and Bayesian optimisation.
How Bayesian inference unifies all of machine learning under one framework: prior beliefs, observed evidence, and posterior distributions over model parameters.
Calibration, reliability diagrams, Expected Calibration Error, temperature scaling, and the full toolkit for quantifying and correcting uncertainty in production ML models.
VAEs combine variational inference with neural networks to learn a probabilistic latent space - enabling generation, interpolation, and disentanglement.
