UO Machine Learning

Undergraduate | 2026

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Area/Catalogue
INFO 3016
Course ID icon
Course ID
203922
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
3
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
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Note:
Course data is interim and subject to change

Course overview

The aim of this course is to develop skills in applying supervised, unsupervised and deep machine learning techniques to real-world data analytics problems. Linear regression, regularization, logistic regression, neural network architectures, neural network learning, convolutional neural network, recurrent neural network, long short-term memory, machine learning in practise, clustering, dimensionality reduction, anomaly detection, large-scale machine learning.

Course learning outcomes

  • Design and implement supervised, unsupervised, and deep machine learning techniques in a range of real-world applications.
  • Analyse different types of data and apply appropriate supervised, unsupervised, and deep machine learning techniques.
  • Evaluate and adapt supervised, unsupervised, and deep machine learning techniques for real-world problem solving.
  • Compare the fundamental issues and challenges of different supervised, unsupervised and deep machine learning techniques.

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A