Machine learning can expand the capabilities of robotic systems including UAV, and applies to a variety of robotic system functions including planning, control, and perception. Robot Learning covers the application of learning techniques including Reinforcement Learning, Learning from Demonstration, Evolutionary, and Robot Shaping that may be used with a variety of machine learning paradigms. A variety of paradigms are available to generate models (e.g., CMAC, lazy learning, LWR, RBF, deep networks). These learning techniques and paradigms are then combined with traditional robotic control approaches (e.g., motor schema, behavior-based, direct and inverse methods) to create controllers to control the robots while operating in real-world environments. This course will explore applying machine learning techniques, paradigms, and control design to robotic systems including UAV. Students will construct a simulation environment for robot system by using machine learning methods.