FIT2111 Symbolic artificial intelligence and machine learning

Intelligent systems need more than models; they need ways to represent problems, reason about possible actions, learn from data, and make decisions responsibly. This unit develops core artificial intelligence capability by combining symbolic artificial intelligence, search, reasoning, planning and machine learning within real-world problem contexts.

You will learn how intelligent agents represent states, goals, constraints and decision processes, and how techniques such as heuristic search, adversarial search, knowledge representation, propositional and first-order logic, and planning support intelligent behaviour. You will also work with machine learning methods, including data representation, supervised learning, unsupervised learning and reinforcement learning, with attention to model complexity, performance, generalisation and suitability for different problems and datasets.

Through problem-based learning activities, you will apply artificial intelligence methods and tools to analyse, design, implement and evaluate intelligent systems for complex scenarios. The unit also develops your ability to reason about the ethical, privacy, security and societal implications of artificial intelligence, and to make responsible choices when selecting, applying and evaluating artificial intelligence techniques.