This course provides an introduction and overview of important concepts and applications in the fields of Machine Learning and Artificial Intelligence (AI). With the availability of fast computers, machine intelligence methods have found widespread applications in areas such as in Big Data and Autonomous Robots. This course will explore some of them, including systems where machine intelligence methods led to significant advancements, often surprising solutions, and sometimes triumphal success.
Availability2020 Course Timetables
- Semester 1 - 2020
- Semester 1 - 2020
On successful completion of the course students will be able to:
1. Critically reflect on ethics, opportunities and risks of current and future developments of Machine Learning and AI.
2. Explain central concepts of Machine Learning and AI.
3. Analyse a given task or data and select suitable Machine Learning and AI methods for processing.
4. Implement relevant code or apply standard libraries for Machine Learning and AI to selected tasks.
5. Produce detailed reports and presentations suitable to support research or business decision-making.
- Artificial Neural Networks and Deep Learning
- Support Vector Machines
- Autonomous Robots
- Search and Prediction in Games
- Evolutionary Algorithms
- Automated Reasoning and Logic
- Aspects of Advanced Machine Learning
This course has similarities to COMP3330. If you have successfully completed COMP3330 you cannot enrol in this course.
The assumed knowledge is equivalent to that of a completed 2nd year Bachelor of Computer Science or a similar degree and should include: basic statistics and mathematics, including mean, standard deviation, vectors, dot product, hyperplanes, basic multivariable calculus, sets and basic first order logic. It also includes some basic programming skills in a language such as Python, Matlab, Java, C# or C/C++. The course will provide a brief mathematics workshop and a quick introduction to Python to refresh some of the assumed knowledge.
Project: Project (multi component)
Written Assignment: Written Assignment (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
It is highly recommended to attend all lectures and labs.
Face to Face On Campus 2.5 hour(s) per Week for Full Term
Self-Directed 10 hour(s) per Week for Full Term
Suggest 8-12 hours time commitment per week (guide only).