|Course code COMP3330||Units 10||Level 3000||Faculty of Engineering and Built EnvironmentSchool of Electrical Engineering and Computer Science|
This course provides an overview about important past and current developments, concepts, and applications in the fast evolving field of machine intelligence. It is an introductory course and could later be extended by higher studies in areas such as, advanced machine learning, data mining, bioinformatics, image processing, optimisation, autonomous agents, computer vision, computer graphics, and related fields. The course's topic is a central part of computer science and software engineering. Many of the concepts addressed by this course were initially biologically motivated and fall under the umbrella of brain theory. The aim is to get an understanding of intelligence, learning, memory, language, and the workings of the human brain by modelling and implementing aspects of these concepts in the computer. With the availability of faster workstations and sophisticated robotic hardware machine intelligence methods can find more widespread applications. This course will address several applications and systems where machine intelligence methods lead to significant advancements, often surprising solutions, and sometimes triumphal success.
Available in 2014
|Objectives||1. Students to understand and apply Artificial Intelligence (AI) techniques; |
2. Students to understand and implement examples of machine learning methods.
3. Students to obtain an overview of past and current developments in machine intelligence.
4. Students to develop the ability to project towards future developments of the field including possible ethical implications in areas such as data mining and robotics.
|Content||1. Machine Learning|
2. Automated Reasoning and Logic
3. Search and Prediction in Games
4. Neural Networks and Brain Mechanisms
5. Evolutionary Algorithms
6. Adaptive Robotics
|Assumed Knowledge||SENG1120, MATH1510 and MATH1110|
|Modes of Delivery||Internal Mode|
|Contact Hours||Computer Lab: for 1 hour(s) per Week for Full Term|
Lecture: for 3 hour(s) per Week for Full Term
|Timetables||2014 Course Timetables for COMP3330|