Machine Learning

Undergraduate | 2026

Course page banner
area/catalogue icon
Area/Catalogue
ARTI 2001
Course ID icon
Course ID
200084
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
2
Study abroad and student exchange icon
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.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
alt
Note:
Course data is interim and subject to change

Course overview

This course provides a foundational understanding of machine learning, covering its core concepts, mathematical foundations, and diverse applications. By the end of the course, learners will be able to understand and implement basic machine learning algorithms and evaluate the algorithms on a variety of datasets using suitable evaluation metrics. This course prepares students to effectively apply machine learning techniques to real-world problems and sets the foundation for more advanced studies in machine learning and artificial intelligence.

  • Supervised Learning
  • Unsupervised Learning
  • Neural Network

Course learning outcomes

  • Describe key concepts in machine learning and their advantages and disadvantages in various applications
  • Demonstrate the principles of simple machine learning algorithms and models including Linear Regression, Naive Bayes, SVM, K-Nearest Neighbour and Decision Trees
  • Evaluate simple machine learning algorithms on both synthetic and real-world datasets using appropriate evaluation metrics
  • Apply data preparation techniques to clean, preprocess, and transform data for machine learning tasks

Prerequisite(s)

Corequisite(s)

N/A

Antirequisite(s)

N/A