Engineering & Robotics

This course applies advanced control theory and optimization techniques to the design and control of complex robotic systems, particularly those with underactuation, integrating signal processing for state estimation and sensing.

Convex optimization provides the mathematical tools for designing optimal controllers, estimating system states, and solving trajectory planning problems in control and dynamical systems, from robotics to fluid dynamics.

Strang's foundational work in linear algebra provides essential tools for understanding and solving problems in convex optimization, particularly in areas like least squares and matrix decompositions.

This course covers the fundamental principles of digital logic design and computer architecture, including instruction set architectures, pipelining, memory hierarchies, and parallel processing, which define the subfield.

This course directly introduces the foundational concepts and methodologies of complex systems science, including emergent phenomena, self-organization, and scaling laws, which define the subfield.