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A course mapper by Monash Association of Coding (MAC)
Symbolic artificial intelligence and machine learning
FIT2111
Synopsis
This unit covers the concepts of intelligent agents and delves into problem-solving and search techniques, including problem representation, heuristic search, and adversarial search. You will learn about knowledge representation and reasoning, focusing on propositional and first-order logic for AI applications, as well as planning. You will also engage with a variety of machine-learning techniques, including data representation, unsupervised and supervised learning and reinforcement learning. The curriculum also addresses the selection of appropriate model complexity tailored to specific problems and datasets. Through problem-based learning activities, you will apply these techniques to real-world scenarios and examine ethical considerations in AI.
Sourced from the Monash Handbook 2026.
Quick facts
- Credit points
- 6
- Level
- 2
- Audience
- Undergraduate
- Type
- Coursework
- School
- Faculty of Information Technology
- Handbook year
- 2026
Prerequisites (2)
What it unlocks (3)
Listed in 1 area of study
- Artificial intelligenceCore units