|Faculty or Section :||Faculty of Health, Engineering and Sciences|
|School or Department :||School of Mathematics, Physics & Computing|
|Grading basis :||Graded|
|Version produced :||1 December 2022|
Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills
One of the most common tasks performed by data scientists and data analysts is machine learning for prediction. This introductory course gives an overview of machine learning including concepts, techniques, and algorithms. The course will give students the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. The course aims at giving a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people for research in data science or practices in data analytics.
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.