COMP SCI 7401 - Introduction to Statistical Machine Learning
Career: | Postgraduate Coursework |
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Units: | 3 |
Term: | 3720 |
Campus: | North Terrace |
Contact: | Up to 2 hours per week |
Available for Study Abroad and Exchange: | Yes |
Available for Non-Award Study: | No |
Pre-Requisite: | COMP SCI 7201 |
Assumed Knowledge: | Basic probability theory and linear algebra |
Assessment: | Written exam and/or assignments |
Syllabus: |
Statistical Machine Learning is concerned with algorithms that automatically improve their performance through "learning". For example, computer programs that learn to detect humans in images/video; predict stock markets, and rank web pages. Statistical machine learning has emerged mainly from computer science and artificial intelligence, and has connections to a variety of related subjects including statistics, applied mathematics and pattern analysis. Applications include image and audio signal analysis, data mining, bioinformatics and exploratory data analysis in natural science and engineering. This is an introductory course on statistical machine learning which presents an overview of many fundamental concepts, popular techniques, and algorithms in statistical machine learning. It covers basic topics such as dimensionality reduction, linear classification and regression as well as more recent topics such as ensemble learning/boosting, support vector machines, kernel methods and manifold learning. This course will provide the students the basic ideas and intuition behind modern statistical machine learning methods. After studying this course, students will understand how, why, and when machine learning works on practical problems. |
Course Fees
Study Abroad student tuition fees are available here
Only some Postgraduate Coursework programs are available as Commonwealth Supported. Please check your program for specific fee information.
The fees displayed below for international students are for students commencing a program in 2024 only. International students who commenced a program in 2023 or prior can find their fee here.
EFTSL | |||
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0.125 |
Course Outline
A Course Outline which includes Learning Outcomes, Learning Resources, Learning & Teaching for this course may be accessed here
Critical Dates
Term | Last Day to Add Online | Census Date | Last Day to WNF | Last Day to WF |
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3720 | Mon 07/08/2017 | Thu 31/08/2017 | Fri 15/09/2017 | Fri 27/10/2017 |
Class Details
Enrolment Class: Lecture | |||||||
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Class Nbr | Section | Size | Available | Dates | Days | Time | Location |
24618 | LE01 | 40 | 15 | 26 Jul - 13 Sep | Wednesday | 11am - 1pm | Engineering Nth, N132, Teaching Room |
4 Oct - 25 Oct | Wednesday | 11am - 1pm | Engineering Nth, N132, Teaching Room |