The Artificial Intelligence specialisation covers modern artificial intelligence across both its symbolic and learning-based traditions. Students study logic-based reasoning, search, planning and optimisation alongside statistical machine learning, deep learning, computer vision and document analysis, and develop the theoretical understanding, technical skills and critical judgement to formulate, build and evaluate AI systems for real-world problems. The specialisation is designed for students aiming for advanced applied or research roles in AI.
Learning Outcomes
- Apply methods of artificial intelligence across symbolic and/or learning-based traditions.
- Formalise real-world problems mathematically and select appropriate AI methods.
- Design, implement and empirically evaluate AI algorithms.
- Critically analyse state-of-the-art AI methods and the ethical, social and data-related implications of their use.
Other Information
This Specialisation is incompatible with the Computing and Mathematical Foundations minor and the Intelligent Systems major.
Students planning to complete COMP4691 Optimisation should ensure they complete MATH1013 or MATH1115 from the Information and Communications Technology-related course list early in their program.
Relevant Degrees
Requirements
This Specialisation requires the completion of 24 units, which must include a minimum of 12 units of 4000-level courses.
A maximum of 12 units from the following list:
COMP2620 Logic
COMP3242 Deep Learning
COMP3620 Artificial Intelligence
COMP3670 Introduction to Machine Learning
A minimum of 12 units from the following list:
COMP4528 Computer Vision
COMP4620 Advanced Topics in Artificial Intelligence
COMP4650 Document Analysis
COMP4670 Statistical Machine Learning
COMP4680 Advanced Topics in Machine Learning
COMP4691 Optimisation
This specialisation is not compatible with the INSY-MAJ.
This specialisation is only available to students studying BAC (AACOM) and BACR&D (AACRD).
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