AMS2002 - Optimisation for Data Science

Year of Study:2 - 4
Credit Units: 3
Duration: 45hours
Prerequisites: AMS1001 Introduction to Linear Algebra and Calculus or with the Instructor’s permission and upon endorsement of the relevant Head or Programme Director. Exclusion: AMS2001 Quantitative Methods for Business Management
Module Description
This module aims to provide students with the optimisation techniques which are widely adopted in data science. Students will be able to acquire techniques to tackle linear programming problems as well as unconstrained and constrained optimisation problems. Real life applications are provided to illustrate the methodologies.
Learning Outcomes
Upon completion of this module, students should be able to:

  1. examine the significance and understand the basic concepts and theories of optimization;

  2. apply linear programming methodologies;

  3. apply unconstrained optimization techniques; and

  4. apply constrained optimization techniques.