Instructor: Chris Wojtan
Teaching Assistant: Camille Schreck
This is an advanced course introducing numerical algorithms with applications in Computer Vision, Machine Learning, Computer Graphics, and Computational Physics. The course will teach students algorithms that solve linear systems and differential equations, paying special attention to the complications created by numerical errors. After completing this course, students should not only be able to solve problems themselves, but also identify strengths and weaknesses in solutions proposed by others.
Textbook: Numerical Algorithms by Justin Solomon. https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf
Target audience: This course is aimed at graduate students in mathematics, computer science, or the natural/physical sciences who wish to better understand computational algorithms for solving difficult mathematical problems.
Pre-requisites: The prerequisites for this course are previous exposure to linear algebra and differential equations.
The final grade will be determined by homework, a project, and class participation.
|27/02/2017||Introduction; numerics and error analysis||Mondi 1|
|Homework 1: numerics and error analysis||03/06/17|
|Homework 2: Linear systems||03/15/17|
|Homework 3: Optimization||03/22/17|
|Homework 4: ODE||04/10/17|
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