Intelligent Autonomous Systems

Catalog description

How can intelligent machines perceive, make decisions, and execute their plans in an uncertain, dynamic world? This course will cover methods and algorithms for sensory perception, planning, and control with a focus on real-time adaptation and learning. Students should have prior experience with data processing and machine learning methods. Topics covered include probabilistic methods for sensory integration, latent models for dynamical systems, navigation and reinforcement learning. Students will learn about fundamental principles underlying algorithms for intelligent systems as well as how to implement these algorithms for real-time processing.