In March of 2016, Google DeepMind's AlphaGo, a computer Go-playing program, defeated the reigning human world champion Go player, 4-1, a feat far more impressive than previous victories by computer programs in chess (IBM's Deep Blue) and Jeopardy (IBM's Watson). AlphaGo combines machine learning approaches, specifically deep neural networks and reinforcement learning, with a technique called Monte Carlo tree search. Current versions of Monte Carlo tree search used in Go-playing algorithms are based on a version developed for games that traces its roots back to the adaptive multi-stage sampling simulation optimization algorithm for estimating the value function in finite-horizon Markov decision processes (MDPs) introduced by Chang et al. (2005), which was the first to use Upper Confidence Bounds (UCBs) for Monte Carlo simulation-based solution of MDPs. We illustrate Monte Carlo tree search by connecting it to simulation optimization through the use of two simple examples: a decision tree and tic-tac-toe.