|Short Description:||Machine Learning|
|Faculty or Section :||Faculty of Health, Engineering and Sciences|
|School or Department :||School of Sciences|
|Student contribution band :||Band 2|
|ASCED code :||020199 - Computer Science not elsewhere|
|Grading basis :||Graded|
|Version produced :||6 December 2021|
Pre-requisite: (STA2300 or STA1003 or STA8170) and CSC1401 or equivalent program and statistical knowledge and skills.
Machine learning is the science of getting computer programs to self-improve performance through experiences. In the past decade, machine learning has given us face and speech recognition, recommender systems for music or movies, self-driving cars, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that people probably use it dozens of times a day without knowing it. In this course, students will learn about the most effective machine learning techniques from a variety of perspectives. Students will also gain practice implementing the machine learning techniques and getting them to work for problem solving. More importantly, students will learn about not only the theoretical underpinnings of learning, but also gain the practical know-how to quickly and powerfully apply these techniques to new problems.