Maths for Machine Learning

Undergraduate | 2026

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Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
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Area/Catalogue
COMP 2006
Course ID icon
Course ID
200083
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Campus
Adelaide City Campus
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course owner
Course owner
Adelaide University
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Course level
2
Work Integrated Learning course
Work Integrated Learning course
No
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No

Course overview

This course aims to equip students with mathematical skills critical for understanding and applying machine learning algorithms. Students will be able to explain and apply the mathematical concepts essential for machine learning, focusing on calculus, linear algebra, optimisation, probability, and information theory. Essential for machine learning and data science curricula, this course strengthens mathematical foundations, supporting advanced studies and practical applications in the field. 

Course learning outcomes

  • Demonstrate the principles of calculus, linear algebra and probability theory and their applications in machine learning
  • Apply gradient-based optimisation techniques to simple multi-variate problems
  • Apply probability theory to model uncertainty and make predictions in machine learning tasks
  • Quantify information content in machine learning systems using information theory principles

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A

Assumed knowledge

N/A

Textbook(s)

N/A

Inherent requirements

N/A

Other resources

N/A

Assessment

N/A

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