This course will cover the basics and implementation of numerical algorithms for scientific computation. I will emphasize the algorithmic aspects more than the theoretical aspects, but a basic understanding of the theory is necessary. The tentative plan of this course is as follows:
Week 1: Introduction, Notation and Examples
Good programming skills (in R, Matlab, or Python); Calculus; Linear Algebra
- Solomon, Justin. Numerical algorithms: methods for computer vision, machine learning, and graphics. CRC press, 2015.
- Atkinson, Kendall, and Han, Weimin. Theoretical numerical analysis. Vol. 39. Berlin: Springer, 2005.
This course is designed for engineering students. Unfortunately, I do not have an engineering background. The examples or applications in the class may not represent what an engineering student may encounter in his/her future career. I will choose examples which are more relevant in machine learning/artificial intelligence and statistics/data sciences.