It is common to assume that players in a game will adopt Nash equilibrium strategies. However, experimental studies have demonstrated that Nash equilibrium is often a poor description of human players' behavior, even in unrepeated normal-form games. Nevertheless, human behavior in such settings is far from random. Drawing on data from real human play, the field of behavioral game theory has developed a variety of models that aim to capture these patterns. This talk will survey over a decade of work on this topic, built around the core idea of treating behavioral game theory as a machine learning problem. It will touch on questions such as: - Which human biases are most important to model in single-shot game theoretic settings? - What loss function should be used to evaluate and fit behavioral models? - What can be learned about examining the parameters of these models? - How can richer models of nonstrategic play be leveraged to improve models of strategic agents? - When does a description of nonstrategic behavior "cross the line" and deserve to be called strategic? - How can advances in deep learning be used to yield stronger--albeit harder to interpret--models? Finally, there has been much recent excitement about large language models such as GPT-4. The talk will conclude by describing how the economic rationality of such models can be assessed and presenting some initial experimental findings showing the extent to which these models replicate human-like cognitive biases.