Bayesian optimization: theory and practice using Python

Chapter 1: Bayesian Optimization Overview

Chapter 2: Gaussian Process

Chapter 3: Bayesian Decision Theory and Expected Improvement

Chapter 4 : Gaussian Process Regression with GPyTorch

Chapter 5: Monte Carlo Acquisition Function with Sobol Sequences and Random Restart

Chapter 6 : Knowledge Gradient: Nested Optimization versus One-shot Learning

Chapter 7 : Case Study: Tuning CNN Learning Rate with BoTorch.