Machine Intelligence

10 Units

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.

Faculty Faculty of Engineering and Built Environment
School School of Electrical Engineering and Computer Science
Availability Semester 1 - 2016 (Callaghan)
Learning Outcomes

On successful completion of this course, students will be able to:

  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.
  1. Machine Learning
  2. Automated Reasoning Logic
  3. Search and Prediction in Games
  4. Neural Networks and Brain Mechanisms
  5. Evolutionary Algorithms
  6. Adaptive Robotics
Assumed Knowledge Equivalent to a completed 2nd year Bachelor of Computer Science
Assessment Items
  • Presentation: History and Philosophy Assignment
  • Written Assignment: Homework Assignment 1
  • In Term Test: Mid Term Test
  • Written Assignment: Homework Assignment 2
  • Formal Examination: Formal Examination *
Contact Hours
  • Computer Lab: for 1 hour(s) per Week for Full Term
  • Lecture: for 3 hour(s) per Week for Full Term
Compulsory Requirements
  • Course Assessment Requirements: 1. Formal Examination: Minimum Grade / Mark Requirement - Students must obtain a specified minimum grade / mark in this assessment item to pass the course. (Students must obtain 40% in the final exam to pass the course.)
Timetable 2016 Course Timetables for COMP6380
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