FIT3192 · Emerging and advanced topics in artificial intelligence

Overview

This advanced undergraduate unit delves into the forefront of Artificial Intelligence, offering you an in-depth exploration of emerging and cutting-edge topics within the field. The course is designed to keep pace with the rapid advancements in AI, providing a comprehensive understanding of both foundational and innovative concepts. Key topics covered in this course include 1) Multi-Agent Systems - Study the dynamics of systems where multiple autonomous agents interact, cooperate, or compete to achieve individual or collective goals; 2) Quantum Machine Learning - Investigate the intersection of quantum computing and machine learning and its potential applications in optimisation, cryptography, and complex data analysis; 3) Cognitive Systems - Examine AI systems that simulate human cognitive processes, including perception, reasoning, learning, and decision-making; and 4) Integrated Planning and Learning: Explore methods that combine planning and learning to enable AI systems to adapt and optimise their strategies in real-time with applications such as robotics, autonomous systems, and complex decision-making scenarios. You will have a robust understanding of these advanced AI topics and be equipped with the knowledge to contribute to the development and application of innovative AI solutions in various domains.

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Demonstrate a comprehensive understanding of new concepts, techniques, and algorithms in the field of AI.
2. Compare and contrast different AI architectures, algorithms and advanced schemes using research-based knowledge and methods.
3. Evaluate the strengths and limitations of recent AI-driven technologies for industry application.
4. Understand the legal and ethical implications of AI on organisations and the future of work
5. Apply technical writing and presentation to effectively communicate advanced topics in AI to a range of academic and expert audiences.

Workload

No workload detail published.

Student experience, not official rules. Nothing here changes what the Handbook says.