- Code STAT7017
- Unit Value 6 units
- Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
- ANU College ANU College of Business and Economics
- Course subject Statistics
- Areas of interest Statistics
This course involves on campus teaching. For students unable to come to campus there will be a remote option. See the Class Summary for more details.
This research-led course provides an introduction to recent developments in Random Matrix Theory and Online Learning that addresses the challenges and opportunities posed by the availability of large amounts of data.
In the first instance, we will review some classic results from multivariate statistical theory, matrix analysis, and probability theory. Then we will present the salient statistical features of big data (e.g., heterogeneity, noise accumulation, spurious correlation, and incidental endogeneity) and show how this impacts on traditional statistical methods and theory.
We follow with an introduction to modern Random Matrix theory and its application in statistics. Applications presented may include topics such as high-dimensional statistical inference, large covariance matrices, large-scale statistical learning through subsampling, sparsification of large matrices, principal component analysis, and dimension reduction.
We conclude with an introduction to the theory of online learning (aka. sequential prediction) to handle the situation of streaming data.
Students will use and learn about the latest computational tools to work with big and streaming data sets. Example data sets may be drawn from areas such finance, web analytics, digital marketing, and satellite imagery data.
Upon successful completion, students will have the knowledge and skills to:
- Explain in detail how statistical features of big data impacts traditional statistical methods and theory;
- Discuss in depth random Matrix theory and its application in statistics on large scale;
- Critically discuss the theory of sequential prediction and management of streaming data; and
- Demonstrate in detail the use of computational tools to work with big and streaming data sets.
- Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4]
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Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.
Requisite and Incompatibility
Information about the prescribed textbook will be available via the Class Summary.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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Offerings, Dates and Class Summary Links
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.