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Unit of study_

Networks and High-dimensional Inference - DATA5441

Year - 2020

In our interconnected world, networks are an increasingly important representation of datasets and systems. This unit will investigate how this network approach to problems can be pursued through the combination of mathematical models and datasets. You will learn different mathematical models of networks and understand how these models explain non-intuitive phenomena, such as the small world phenomenon (short paths between nodes despite clustering), the friendship paradox (our friends typically have more friends than we have), and the sudden appearance of epidemic-like processes spreading through networks. You will learn computational techniques needed to infer information about the mathematical models from data and, finally, you will learn how to combine mathematical models, computational techniques, and real-world data to draw conclusions about problems. More generally, network data is a paradigm for high-dimensional interdependent data, the typical problem in data science. By doing this unit you will develop computational and mathematical skills of wide applicability in studies of networks, data science, complex systems, and statistical physics.

lectures 2 hrs/week, tutorial/computational lab 2 hrs/week

report (15%), oral presentation (15%), tutorial and laboratory assignments (30%), final exam (40%)

Assumed knowledge
Linear algebra (matrices, eigenvalues, etc.); introductory concepts in statistics (statistical models, inference); a programming language.


Faculty: Science

Semester 1

24 Feb 2020

Department/School: Mathematics and Statistics Academic Operations
Study Mode: Normal (lecture/lab/tutorial) day
Census Date: 31 Mar 2020
Unit of study level: Postgraduate
Credit points: 6.0
EFTSL: 0.125
Available for study abroad and exchange: Yes
Faculty/department permission required? No
More details
Unit of Study coordinator: Oded Yacobi
HECS Band:
Courses that offer this unit

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