NA/EECS 568, ROB 530:
Mobile Robotics: Methods and Algorithms
Theory and application of probabilistic and geometric techniques for autonomous mobile robotics. This course presents and critically examines contemporary algorithms for robot perception. Topics include Bayesian filtering; stochastic representations of the environment; motion and sensor models for mobile robots; algorithms for mapping and localization; application to autonomous marine, ground, and air vehicles.
NA 565, ROB 535, ME 599:
Self Driving Cars: Perception and Control
Self-driving cars are a transformative technology for society. This course covers the underlying technologies in perception and control. Topics include deep learning, computer vision, sensor fusion, localization, trajectory optimization, obstacle avoidance, and vehicle dynamics. The course includes theoretical underpinnings of self-driving car algorithms and practical application of the material in hands-on labs.
Computational Linear Algebra
New Course - Fall 2020-2021
Linear algebra and computation as a means for reasoning about data and making discoveries about the world. Topics: The Julia programming language. Systems of linear equations. Vectors, matrices, inverses. Regression. Matrix factorization. Spatial coordinates. Cameras, LiDARS, accelerometers, single-axis gyroscopes, encoders. Optimization and robot perception. What is an ODE.