Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject is not offered in 2016.
|Time Commitment:||Contact Hours: Contact Hours: 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week. |
Total Time Commitment:
Estimated Total Time Commitment - 170 hours
Study Period Commencement:
MAST90082 can be taken concurrently.
|Recommended Background Knowledge:|| |
Study Period Commencement:
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
Multivariate statistics concerns the analysis of collections of random variables that has general applications across the sciences and more recently in bioinformatics. It overlaps machine learning and data mining, and leads into functional data analysis. Here random vectors and matrices are introduced along with common multivariate distributions. Multivariate techniques for clustering, classification and data reduction are given. These include discriminant analysis and principal components. Classical multi-variate regression and analysis of variance methods are considered. These approaches are then extended to high dimensional data, such as that commonly encountered in bioinformatics, motivating the development of multiple hypothesis testing techniques. Finally, functional data is introduced.
After completing this subject students should gain:
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
Doctor of Philosophy - Engineering |
Master of Philosophy - Engineering
Master of Science (Mathematics and Statistics)
Mathematics and Statistics |
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