MATH 5804 - Topics in Operations Research, Winter 2021

Modern applications of mathematical programming: from machine learning to mathematical art

Kevin Cheung, Carleton University

Description

In this course, we will look at a number of modern applications of mathematical programming. Areas of interest include 2D packing, mathematical art, image processing, design of experiments, and machine learning. The Julia programming language will be introduced and used in assignments.

Evaluation:

The final grade will be based on weekly assignments and a final project.

Prerequisites

Undergraduate linear algebra, multivariate calculus, and computer programming. Knowledge of linear programming highly recommended but not required.

Some references for the course

  1. Deep learning by I. Goodfellow, Y. Bengio, and A. Courville.
  2. Linear algebra and optimization for machine learning by C. C. Aggarwal.
  3. Machine learning under a modern optimization lens by D. Bertsimas and J. Dunn.
  4. Model building in mathematical programming by P. Williams.
  5. OptArt by R. Bosch.
  6. Introduction to nonlinear and global optimization by E.M.T. Hendrix and B.G.-Tóth
  7. Algorithms for optimization by M.J. Kochenderfer and T.A. Wheeler