42.520 Optimisation for Data Science

Course description

The course will start by providing basic principles of linear optimisation and gradually cover parts of discrete, convex, robust, non-linear optimisation methods and algorithms. Topics used extensively in data and science and machine learning to be covered include linear programming, duality, gradient descent, sensitivity analysis, two-player zero-sum games, integer programming, branch, and bound methods, backpropagation, and so on. Throughout the course, a number of applications from various areas will be discussed.

Instructor

Carlos Gerardo Murguia Rendon, Cai Yutong

Number of credits: 12