This course provides an overview of 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, deep learning, 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 develop 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.
- Semester 1 - 2020
On successful completion of the course students will be able to:
1. Apply Artificial Intelligence (AI) techniques.
2. Demonstrate their understanding and apply examples of machine learning methods.
3. Explain past and current developments in machine intelligence.
4. Demonstrate the ability to project towards future developments of the field including possible ethical implications in areas such as data mining and robotics.
Neural Networks and Brain Mechanisms
Search and Prediction in Games
Automated Reasoning and Logic
Project: Project (multi component)
Written Assignment: Written Assessment (multi component)
Formal Examination: Formal Examination *
* This assessment has a compulsory requirement.
In order to pass this course, each student must complete ALL of the following compulsory requirements:
Course Assessment Requirements:
- Formal Examination: Minimum Grade / Mark Requirement - Students must obtain a specified minimum grade / mark in this assessment item to pass the course. - Students whose overall mark in the course is 50% or more, but who score less than 40% in the compulsory item and thus fail to demonstrate the required proficiency, will be awarded a Criterion Fail grade, which will show as FF on their formal transcript. However, students in this position who have scored at least 25% in the compulsory assessment item will be allowed to undertake a supplementary 'capped' assessment in which they can score at most 50% of the possible mark for that item.
Face to Face On Campus 1.5 hour(s) per Week for Full Term
Face to Face On Campus 2.5 hour(s) per Week for Full Term