In this class, we’ll develop ways to think about and solve probabilistic inference problems with robots. Each week we'll introduce a new real-world challenge, and students will work in teams to reason about the math behind the task, design a method, and implement it in on physical robots! Through this process, we’ll actually arrive at methods that are both fundamental and very modern, used as the basis for contemporary artificial intelligence, machine learning, signal processing and communication. The course material will build from basic principles of probability to concepts such as hypothesis testing, the EM algorithm, and Bayesian filtering. In parallel we'll develop methods for robot localization, optimal control, and planning. The class will have minimal lecture, and will mostly consist of round-table discussion and programming with real robots.

An introduction to nuclear weapons design, including relatively recent developments. Discusses the involved physics, chemistry, and engineering. Additionally, the course will address both the science and impact of nuclear reactors, enrichment, fusion, and ethics.