Introduction to Machine Learning

6 unitsSem 2In Person

School of Computing · ANU College of Systems and Society

About this course

Essential foundations for any machine learning application are a basic statistical analysis of the data to be processed, a solid understanding of the mathematical foundations underpinning machine learning as well as the basic classes of learning/adaptation concepts. Those foundations are bundled in this single, introductory course to machine learning in preparation for deeper explorations into the topic, but also as a standalone unit.

Learning outcomes

1Develop an appreciation for what is involved in learning via data-driven approaches, like data collection, data safety and privacy, and ethics in machine learning.
2Describe a variety of machine learning tasks, like regression and classification, clustering, dimensionality reduction.
3Understand how to formalise practical problems and evaluate model performance of machine learning tasks.
4Interpret mathematical equations from linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
5Design mathematical and statistical models using linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
6Analyse advanced machine learning technologies including reinforcement learning, neural networks, and large models

Assessment

Weights and hurdle requirements come straight from the imported ANU record.

Assignments

Learning outcomes 1, 2, 3, 4, 5, 6

40%

Final Exam

Learning outcomes 1, 2, 4, 5, 6

60%

Study expectations

Study information

130 hours including lectures and tutorials, assessments and private study

Texts and reading

None

Related courses

COMP3670

Related ANU course

Co taught

Course essentials

Academic year: 2026
Course subject: COMP
Subject name: Computer Science
Academic career: PGRD
School: School of Computing
College: ANU College of Systems and Society
Convener: Dr Diego Marcondes - Dr Yun Kuen Cheung
Delivery: In Person
Unit value: 6 units
EFTSL: 0.125
Last source update: Not listed

View the ANU course source

Fees

Domestic fee paying students: Domestic · 2026 · Course basis; $5,520
International fee paying students: International · 2026 · Course basis; $7,020
Student Contribution Band Code 2: Commonwealth supported · 2026; ANU contribution-band code 2

Areas and attributes

Areas of interest

RoboticsIntelligent SystemsArtifical IntelligenceComputer Systems