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  3. Introduction to Machine Learning

Introduction to Machine Learning (COMP90049)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
Semester 2
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AIMS

Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.

INDICATIVE CONTENT

Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.

Intended learning outcomes

On completion of this subject students are expected to be able to:

  • ILO 1 - Apply elementary mathematical concepts used in machine learning
  • ILO 2 - Derive machine learning models from first principles
  • ILO 3 - Design, implement, and evaluate machine learning systems for real-world problems
  • ILO 4 - Identify the correct machine learning model for a given real-world problem

Generic skills

On completion of this subject, students should have the following generic skills:

  • Ability to communicate effectively within both the engineering team and the community at large 
  • The ability to undertake problem identification, formulation, and developing solutions especially exploiting acquired data 
  • Integration of Data Processing tools to build more complex software systems 
  • As a result the student will develop skills to utilise a systems approach to complex problems 

Last updated: 11 July 2025