# ROB 501: Mathematics for Robotics

ROB 501: Mathematics for Robotics, is a graduate-level course at the University of Michigan that introduces applied mathematics for robotics engineers.

Topics include vector spaces, orthogonal bases, projection theorem, least squares, matrix factorizations, Kalman filter and underlying probabilistic concepts, norms, convergent sequences, contraction mappings, Newton Raphson algorithm, local vs global convergence in nonlinear optimization, convexity, linear and quadratic programs.

An initial set of topics was created by interviewing the faculty in Robotics in May 2014. The list was culled down to the final set of topics over summer and fall 2014 while lecture notes, HW sets, and exams were being composed.

Course GitHub Repository (Lecture Notes, HW sets)

Textbookâ€™s GitHub Repository (Latex Source Files, Old Exams)

YouTube Lectures Fall 2018 (Professor Jessy Grizzle)

**Note for Potential Adopters** The course scales well. One instructor and a single graduate teaching assistant is able to handle 100 students per semester. An additional assistant is recommended for each additional 50 students.