Mathematics for Computing
Overview
This unit aims to introduce students to the fundamental mathematical concepts that underpin computer science, data analysis, AI and cybersecurity. It equips students with essential mathematical techniques in discrete mathematics, statistics, probability, and linear algebra. Students will develop analytical and problem-solving skills to model, interpret, and reason about data and systems, laying a strong foundation for further study and professional practice in digital and intelligent technologies.
Requisites
01-November-2026
Unit learning outcomes
Students who successfully complete this unit will be able to:
- Convert between binary, hexadecimal and ASCII representations of data.
- Apply Boolean algebra to solve logic problems.
- Use probability theories to model uncertainty and support decision-making in intelligent systems.
- Calculate statistical measures and interpret data using statistical software.
- Describe relationships between variables using correlation, regression, and techniques of statistical inference, using key terms appropriately.
- Apply concepts in vectors and matrices to problems in data science and cyber security contexts.
- Interpret and solve mathematical problems by selecting appropriate techniques and tools and communicate solutions effectively.
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
On-campus |
2.00 | 12 weeks | 24 |
| On-campus Class |
2.00 | 6 weeks | 12 |
| On-campus Lab |
2.00 | 6 weeks | 12 |
| Total | 48 |
Assessment
| Type | Task | Weighting | ULOs |
|---|---|---|---|
| Written Assignment | Individual/Group | 10-30% | 4,5 |
| Examination | Individual | 40-60% | 1,2,3,4,5,6,7 |
| Laboratory Tutorial Quizzes | Individual | 10-30% | 1,2,3,4,5,6,7 |
| Mid-Semester Test | Individual | 10-20% | 1,2,3 |
Hurdle
As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:
40% on exam - standard for all maths unit
Content
Discrete mathematics: Set theory, binary numbers, logic and Boolean algebra, hexadecimal/ascii, combinatorics.
Probability: Basics of probability (dependent/independent events, expected values, continuous/discrete variables) Conditional probability, Bayes’ Theorem, Probability distributions (Binomial, Gaussian, Poisson, Bernoulli, Exponential)
Statistics: Descriptive statistics, correlation and regression, error and the normal distribution, hypothesis testing (for single samples and to compare groups).
Linear Algebra: Vectors (cosine similarity and applications to AI), Matrices, Markov Chains and applications, Tensors, Linear transformations
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.