Bachelor of Science (Honours) in Applied Artificial Intelligence at Multimedia University (MMU)

Program Structure: A Step-by-Step Path to Applied AI Expertise

The programme is structured across three years, combining laboratory-based learning, applied projects, and industrial exposure. :cite:contentReference[oaicite:2]{index=2}

• Year 1 – Foundations in AI & Computing
  • Fundamentals of Computer Systems
  • Data Communications and Networking
  • Artificial Intelligence Fundamentals
  • Database Systems
  • Data Acquisition, Engineering and Visualization
  • AI Governance & Ethics
  • Probability & Statistics
• Year 2 – Applied AI Engineering
  • Software Engineering
  • Machine Learning Concepts and Technologies
  • Algorithms and Data Structures for AI
  • Embedded Systems for AI
  • Machine Vision and Image Processing
  • Project Management for AI Applications
  • Bespoke Industrial Studio
  • BYOC Electives
• Year 3 – Advanced AI, Deployment & Industry
  • Natural Language Processing
  • Deep Learning and Generative AI Technology
  • Robotics & Perception
  • Cloud Computing Technology
  • IoT Systems and Applications
  • Industrial Training
  • Project I & Project II
  • BYOC Electives

Entry Requirements

Applicants may qualify through Foundation/Matriculation, STPM, A-Level, UEC, Diploma (Computing or Non-Computing), TVET routes, or other recognised equivalent qualifications. A Mathematics requirement at SPM level (or equivalent) applies; students who do not meet it may need to take reinforcement Mathematics. :cite:contentReference[oaicite:3]{index=3}

Career Opportunities

Graduates are prepared for roles across applied AI and intelligent systems development. :cite:contentReference[oaicite:4]{index=4}

• AI Specialist
• Machine Learning Developer
• Embedded AI Developer
• Robotics and Perception Specialist
• IoT and AI Solutions Developer
• Data Science Practitioner
• Computer Vision Specialist
• AI Solutions Consultant

Program Highlights

• Duration: 3 years (Full-time). :cite:contentReference[oaicite:5]{index=5}
• Applied Focus: Real-time AI deployment, edge/embedded AI, and cyber-physical systems emphasis. :cite:contentReference[oaicite:6]{index=6}
• Hands-on Learning: Laboratory-based courses, applied projects, and industry collaboration elements. :cite:contentReference[oaicite:7]{index=7}
• Broad AI Engineering Coverage: IoT, cloud computing, machine vision, embedded AI solutions. :cite:contentReference[oaicite:8]{index=8}