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Analysis of High-Dimensional Data (MAST90110)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
Modern data sets are growing in size and complexity due to the astonishing development of data acquisition and storage capabilities. This subject focuses on developing rigorous statistical learning methods that are needed to extract relevant features from large data sets, assess the reliability of the selected features, and obtain accurate inferences and predictions. This subject covers recent methodological developments in this area such as inference for high-dimensional inference regression, empirical Bayes methods, model selection and model combining methods, and post-selection inference methods.
Intended learning outcomes
After completing this subject students should gain:
- A deeper understanding of statistical methods for high-dimensional data and their properties
- The ability to apply such methods using a statistical computing package and interpret the results
Generic skills
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
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments;
- computer skills: the ability to use statistical computing packages.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
and one of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Any of the following:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90085 | Multivariate Statistical Techniques | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90083 | Computational Statistics and Data Mining | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90084 | Statistical Modelling | Semester 1 (On Campus - Parkville) |
12.5 |
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
- Two written assignments amounting up to 50 pages, due mid and late semester (20%)
- 3 hour written examination, held in examination period (60%)
- Project of 1,500 words, due end of semester (20%)
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Davide Ferrari Mode of delivery On Campus (Parkville) Contact hours 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week Total time commitment 170 hours Teaching period 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017 Semester 2 contact information
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Course Master of Data Science - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Last updated: 3 November 2022