Effective black-box optimization is a widespread challenge in many real-world applications, including optimal experimental design in science and engineering (e.g., robotic control, molecular engineering, and drug discovery). Thus far, Bayesian optimization (BO) has been a state-of-the-art technique, and among the most popular approaches that empower these modern applications. This tutorial provides a comprehensive starting point for researchers and practitioners interested in the theory and practice of real-world active learning and Bayesian optimization.