My talk will focus on theoretical and algorithmic ideas in machine learning and AI, and their origin in problems of robotics. Much of my talk will focus on no-regret online learning methods in machine learning and the critical role of interaction for learning in robotics. I will highlight th tremendous impact robotics has had in identifying key learning problems and suggesting algorithmic techniques; additionally, I'll consider the remarkable tools that have been developed within AI and learning to address hard robotics problems. I'll discuss a variety of machine learning techniques of increasing sophistication from the most familiar classification problems, to structured prediction, and to imitation learning. I will also address ho to make reinforcement learning and learning control practical in robotics. Throughout, we will look at case studies in learning dexterous manipulation, activity forecasting of drivers and pedestrians, and imitation learning of robotic locomotion and rough-terrain navigation. These case studies highlight key challenges in applying AI and learning algorithms in practical settings.