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SIT787 - Mathematics for Artificial Intelligence


2020 unit information

Important Update:

Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.

Last updated: 2 June 2020

Enrolment modes:Trimester 1: Waurn Ponds (Geelong), Cloud (online)
Trimester 2: Waurn Ponds (Geelong), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Asef Nazari
Trimester 2: Asef Nazari




Incompatible with:


Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

3 x 1 hour class per week, 1 x 1 hour workshop per week

Scheduled learning activities - cloud:

1 x 1 hour scheduled online workshop per week


This unit provides the fundamental mathematical and statistical knowledge to understand important concepts in Artificial Intelligence (AI) and Data Science (DS). The contents of the unit are selected carefully to cover the most frequent mathematical and statistical tools and techniques to help students easily learn technical topics in AI and DS, enabling students to obtain enough experience to expand their knowledge into new directions if required. The unit builds a strong bridge between simple and core mathematical and statistical concepts and advanced techniques that are used in developing modern algorithms in AI and DS.


These are the Learning Outcomes (ULO) for this Unit
At the completion of this Unit
successful students can:

Deakin Graduate Learning Outcomes


Explain the role and application of mathematical concepts associate with artificial intelligence.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving


Identify and summarise mathematical concepts and technique covered in the unit needed to solve mathematical problems from artificial intelligence applications.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving


Verify and critically evaluate results obtained and communicate results to a range of audiences

GLO2: Communication
GLO4: Critical thinking


Read and interpret mathematical notation and communicate the problem-solving approach used

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication

These Unit Learning Outcomes are applicable for all teaching periods throughout the year


Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week
Problem Solving Task Written responses to mathematical problems 10% Week 4
Problem Solving Task 2 Written responses to mathematical problems 15% Week 8
Problem Solving Task 3 Written responses to mathematical problems 15% Week 11
Examination 2-hour written examination 60% Examination period

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Hurdle requirement

To be eligible to obtain a pass in this unit, students must achieve a mark of at least 50% in the examination.

Learning Resource

There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.

Unit Fee Information

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