This paper covers machine learning algorithms such as the ones implemented in the WEKA machine learning workbench, including techniques that deliver state-of-the-art predictive performance.
Three 300 level Computer Science papers, including
COMP321 Practical Data Mining or
COMP316 Artificial Intelligence Techniques and Applications
COMP521 Machine Learning Algorithms
COMP421 Moodle Page
Official Timetable Information
10 hours per week, with two hours of lectures, and (on average) 8 hours spent working on the assignments and revising lecture notes.
Witten, I.H., Frank, E., and Hall, M. (2014) Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufman.
Labs 6 and 7 in R block
Internal assessment/final examination ratio 1:0
Each of the two assignments is worth 30% of the total marks for the course. Each of the two in-class tests is worth 20% of the total marks.
Slides used in class will be made available on the course web page, and we will closely follow the text book. However, there will be additional explanations given in class that you may find useful.
Follow this link for Academic Integrity information and this link for detailed explanation of How to prevent plagiarism in Computer Science assessment items.
Follow this link for information on Performance Impairment.
Student Concerns and Complaints
Follow this link for Student Concerns and Complaints information.
Application for Extension
Follow this link for information on applying for an Extension.
Review of Grade
Follow this link for information on applying for a Review of Grade.
Your attention is drawn to the following regulations and policies, which are published in the University Calendar: