MATH 7017 Probabilistic Graphical Models
Credit Points 10
Legacy Code 301365
Coordinator Oliver Obst Opens in new window
Description Modelling data provides us with a method for inference, but there are many occurrences when interest lies in the reasoning behind the decision making. In this unit, students learn to model processes and the reasoning behind the processes using probabilistic graphical models. The unit investigates the construction and application of model-based approaches for complex systems. Students will manually create models based on prior knowledge and investigate methods of learning model structures from data, which can be used to make decisions under uncertainty. Topics covered include Monte Carlo Methods, Decision Theory, Bayesian networks, Markov networks, and the use of information theory.
School Computer, Data & Math Sciences
Student Contribution Band HECS Band 1 10cp
Level Postgraduate Coursework Level 7 subject
Pre-requisite(s) MATH 7016
Probability, Linear Algebra, Basic Programming.
On successful completion of this subject, students should be able to:
- Manually construct probabilistic models for specific data.
- Automatically construct probabilistic models by learning from data.
- Use the models to make decisions under uncertainty.
- Accurately represent a probabilistic model using a graphical representation.
1.Network representation and graphical models
2.Probabilistic models and entropy
3.Inference in graphical models
4.Learning graphical models
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
|Applied Project||15 pages||40||N||Individual|
|Intra-session Exam||2 hours||30||N||Individual|
Parramatta - Victoria Rd
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