Classification algorithms, supervised learning, neural networks, generative models, and multivariate linear regression! The terminology of machine learning can be intimidating... then you get to the math. Machine learning doesn't need to live in the halls of academia however, and this session aims to answer some of the questions you have about machine learning. In this session, you'll see how to use generic algorithms to make "intelligent" predictions about sets of data. How that algorithm can be reused across domains, to predict entirely different scenarios. Then we'll get into the fun stuff, using deep learning to recognize objects. You'll also learn where machine learning can fall short and when it can produce less than desirable results. And you'll also see that sometimes we have no idea how the machine gets the answer ... it just does! By the end of this session, you'll have a foundational level of understanding of machine learning to continue your explorations in this exciting and emerging field!