This course introduces 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: Regular assignments and class participation.
Teaching format: Lectures, self-study, and in-depth discussions. Weekly homeworks
ECTS: 3 Year: 2022
Track segment(s):
Elective
Teacher(s):
Christopher Wojtan
Teaching assistant(s):
Peter Synak
If you want to enroll to this course, please click: REGISTER
The book:
The course follows selected chapters from the book Numerical Algorithms by Justin Solomon. Several physical copies of the book are available in the library, the book is also available for free as a pdf on Justin's website here.
- Teacher: Chris WOJTAN
- Teaching Assistant: Peter Synak