Numerical Analysis and Elements of Machine Learning

Catalog description

The course will include numerical methods for derivatives approximation; will teach data approximation and interpolation by Fourier series; Euler's and Runge-Kutta's methods for solving ordinary differential equations (ODE) and systems of ODE. Also, the students will study methods to approximate solutions of partial differential equations (PDE) and will learn the basics of optimization (minimization of functions) for machine learning (ML). The students will develop the important skills of knowledge and methods generalization for their computer implementation and will program the basic methods in MatLab. Some programming skills would be of help. Prerequisites: MATH 2414 . Crosslisted with: CSCI 546 .