ITI9004 - Mathematical foundations for data science and AI

There is a more recent version of this academic item available.

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.

Requisites

Prohibition

Rules

Enrolment Rule

Notes

This unit is only available to students enrolled into C6009 Master of Data Science taught at the Indonesia campus.

This unit is not offered in 2021.

Learning outcomes

On successful completion of this unit, you should be able to:

Use trees and graphs to solve problems in computer science;

Apply counting principles in combinatorics;

Describe the principles of elementary probability theory, evaluate conditional probabilities and use Bayes' Theorem;

Demonstrate basic knowledge and skills of linear algebra, including the manipulation of matrices, solution of linear systems, and evaluate and apply determinants;

Explain fundamental concepts in calculus including basic differentiation and integration, and composite, inverse and parametric functions;

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.

Teaching approach

Peer assisted learning

Assessment

1 - Continuous assessment: part 1

Value %:10

Hurdle type:Threshold

Hurdle description:

This task has a threshold mark hurdle, which means you must achieve at-least 45% of the available marks in the examination, at-least 45% in total non-examination assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

2 - Continuous assessment: part 2

Value %:15

Hurdle type:Threshold

Hurdle description:

3 - Continuous assessment: part 3

Value %:15

Hurdle type:Threshold

Hurdle description:

4 - Examination (3 hours and 10 minutes)

Value %:60

Hurdle type:Threshold

Hurdle description:

Scheduled and non-scheduled teaching activities

Applied sessions

Lectures

Workload requirements

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.

Learning resources

Recommended resources