COIT13122 Machine Intelligence

Course Description

This course provides a basic understanding of the concepts, techniques and general overview of research and development in modern Machine Intelligence. 1. Intelligent agents: agent; structure; environment. 2. Problem solving: Search Strategies, simulated annealing. 3. Game playing: alpha-beta Pruning, game programs. 4. Machine learning: learning from observation, learning by training neural networks, supervised learning, unsupervised learning, learning by simulating evolution - evolutionary algorithms. 5. Pattern recognition: structure of pattern recognition systems, character recognition, image recognition, speech recognition. 6. Robotics: history, classification, architectures, configuration spaces, navigation, motion planning, robotic control. (This course was previously named 'Machine Intelligence B'.)

Course at a glance

Faculty: Faculty of Informatics and Communication
Career: Undergraduate
Credit points: 6
Requisites: Prerequisite: COIT 11134 or COIT 11135
HECS Banding: 2
EFTSL 0.125

Course Availability

Term Campuses
T3 FLEX: ROK