Bayesian optimization (BO) has emerged as an exciting sub-field of machine learning and artificial intelligence that is concerned with optimization using probabilistic methods. Systems implementing BO techniques have been successfully used to solve difficult problems in a diverse set of applications, including automatic tuning of machine learning algorithms, experimental designs, and many other systems. Several recent advances in the methodologies and theory underlying BO have extended the framework to new applications and provided greater insights into the behavior of these algorithms. Bayesian optimization is now increasingly being used in industrial settings, providing new and interesting challenges that require new algorithms and theoretical insights. Therefore, I think having a tutorial on Bayesian optimization for ACML audience is timely, useful, and practical for both academia and industries to know the recent advances on Bayesian optimization in a systematic manner. The topics of this tutorial consists of two main parts. In the first part, I will go into detail the BO in the standard setting. In the second part, I will present the current advances in Bayesian optimization including (1) batch BO, (2) high dimensional BO and (3) mixed categorical-continuous BO. In the end of the talk, I also outline the possible future research directions in Bayesian optimization.