Machine Learning
COMP3010
Option

Year 3, Sem 1 Option Elective Enabling Knowledge and Skills Technical Competence Engineering Application Experience

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Code COMP3010
Credits 25
Graduate Attributes

Introduction

This unit introduces machine learning (ML), a core component of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions. Covering foundational algorithms and practical applications, students will engage with supervised, unsupervised, and deep learning techniques. The course emphasizes the design, analysis, and implementation of ML models, incorporating case studies to highlight real-world applications in various domains. By the end, participants will acquire the skills to apply ML methods effectively, paving the way for innovations in technology and beyond.
Lecture 1 x 2 Hours Weekly 
Tutorial 1 x 2 Hours Weekly 

Unit Learning Outcomes

  • 1 Understand fundamental concepts in machine learning, GC1. GC3, GC6
  • 2 Design, evaluate and apply conventional machine learning methods for data analysis, GC1, GC2, GC3, GC6
  • 3 Train, test and apply deep learning methods for data analysis, GC1, GC2, GC3, GC6
  • 4 Apply machine learning to analyse and solve real-world problems, GC1, GC2, GC3, GC6
  • 5 Establish industry connection and career capability, such as demonstrate, visualise, communicate data analysis results, GC1, GC3, GC4, GC6

Course Learning Outcomes

  • 1 Apply the theory of computer systems networking within the engineering design method, whilst remaining cognisant of network operational issues
  • 2 Define a problem and apply creative thinking to the formulation of a solution; verify and implement the solution
  • 5 Use appropriate tools to design, analyse and verify computer systems networks and their components

Assessment Breakdown

Recent Unit Changes & Response to Student Feedback

Students are encouraged to provide feedback through student surveys (such as Insight and the annual Student Experience Survey) and interactions with teaching staff.

Listed below are some recent changes to the unit as a result of student feedback.

2024

Significant re-development of the unit, including lectures, tutorials and assessments. Tutorials increased from 1h to 2h.