ITI9004 · Mathematical foundations for data science and AI

Overview

Mathematical topics fundamental to computing and statistics including trees and other graphs, counting in combinatorics, principles of elementary probability theory, linear algebra, and fundamental concepts of calculus in one and several variables.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Use trees and graphs to solve problems in computer science;
2. Apply counting principles in combinatorics;
3. Describe the principles of elementary probability theory, evaluate conditional probabilities and use Bayes' Theorem;
4. Demonstrate basic knowledge and skills of linear algebra, including the manipulation of matrices, solution of linear systems, and evaluate and apply determinants;
5. Explain fundamental concepts in calculus including basic differentiation and integration, and composite, inverse and parametric functions;
6. Perform key skills in the calculus of functions of several variables including the calculation of partial derivatives, find tangent planes and identify stationary points, root findings and convexity for optimisation.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Student experience, not official rules. Nothing here changes what the Handbook says.