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 |