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Welcome!
CS 329 is a graduate-level course that explores mobile robotics from a statistical perspective. Mobile robotics is one of the hot areas in artificial intelligence. In recent years, statistical techniques have changed the face of mobile robotics in many ways, providing robust new solutions to hard robotic problems, involving sensor uncertainty. Apart from being robust in practice, these statistical techniques also have a sound mathematical basis that makes it easy to understand the virtues and limitations of these approaches. The course should be of interest to anyone seeking to develop robust robot software, and anyone who is interested in real-world applications of statistical theory. Students participating in this course will acquire the skill of developing robust software for robots operating in real-world environments, and understanding the mathematical underpinnings of their software. Even though this course focuses on mobile robotics, the techniques covered in this course apply to a much brooder range of embedded computer systems, equipped with sensor and actuators. The course involves three types of activities:
Prerequisites: This is an advanced graduate level course. Familiarity with basic statistical concepts (Bayes rule, PDFs, Kalman filters, continuous distributions...) will be helpful for this course, as will be hands-on experience with software development in C or C++. But the most important prerequisite will be creativity and enthusiasm. |