Game AI developers tend to treat machine learning with a mixture of curiosity, skepticism and dismissal. There's a widespread perception that certain unique aspects of game AI make it a poor fit for ML. However, game AI is far from unique in its needs. The generally bad experiences game AI has had with ML are primarily due to a lack of knowledge about the necessary tools and techniques rather than a failing of the technique itself. Simply understanding these tools and techniques, however, often helps game devs get beyond the suspicion and reluctance and use machine learning techniques to improve their games. This lecture will describe key concepts from ML, such as over-fitting and the bias/variance tradeoff, and the problem spaces (supervised/unsupervised, classification versus regression, etc.). We will then give a quick overview of several common and valuable ML algorithms.