Mathematics for Robotics | University of Michigan

Comprehensive graduate-level course on applied mathematics for robotics engineers, covering topics like vector spaces, Kalman filter, and nonlinear optimization.

Introduction

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

Highlights

• 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.
• Lecture videos, textbook, lecture notes, and recitation questions/answers are all available.
• Covers a wide range of mathematical concepts relevant to robotics.

Recommendation

This course is recommended for graduate students in robotics or related fields who want to gain a strong foundation in the mathematical principles and techniques used in robotics. The course covers a comprehensive set of topics and provides valuable resources for self-study.