This is an advanced course introducing numerical algorithms with applications in Computer Vision, Machine Learning, Computer Graphics, Robotics, and Computational Physics. The course will teach students algorithms that solve linear systems, root finding problems, continuous optimization problems, and ordinary differential equations, with special attention given to the complications created by numerical errors. After completing this course, students should not only be able to implement and apply common numerical algorithms, but also identify strengths and weaknesses in solutions proposed by others.

Target group: Graduate students in mathematics, computer science, or the natural/physical sciences who wish to better understand computational algorithms for solving difficult mathematical problems.

Prerequisites: Programming experience and undergraduate coursework on linear algebra and differential equations.

Evaluation: Completion of homeworks and class participation.

Teaching format: Two lectures per week, with regular homeworks.

ECTS: 3 Year: 2019

Track segment(s):
CS-NUM Computer Science - Visual and Numerical Computing
DSSC-NUM Data Science and Scientific Computing - Numerical Computing

Teacher(s):
Christopher Wojtan

Teaching assistant(s):
Georg Sperl

If you want to enroll to this course, please click: REGISTER