MATH 3011 Probabilistic Models and Inference
Credit Points 10
Legacy Code 301250
Coordinator Oliver Obst Opens in new window
Description The subject provides students with an understanding of probabilistic models and inference. It covers model-based approaches for complex systems - from constructing these models to applying information to models. The models, which can be created manually and obtained by learning from data, will also be useful to make decisions under uncertainty. A variety of models and techniques will be discussed; examples include Monte Carlo Methods, Decision Theory, Bayesian networks, Markov networks, and the use of information theory.
School Computer, Data & Math Sciences
Discipline Statistics
Student Contribution Band HECS Band 1 10cp
Check your HECS Band contribution amount via the Fees page.
Level Undergraduate Level 3 subject
Pre-requisite(s) MATH 1033
OR
MATH 1028
AND
COMP 1013
OR
COMP 2023
Assumed Knowledge
Probability, Linear Algebra, Basic Programming.
Learning Outcomes
- 1. Manually construct probabilistic models for specific data.
- 2. Automatically construct probabilistic models by learning from data.
- 3. Use the models to make decisions under uncertainty.
- 4. Accurately represent a probabilistic model using a graphical representation.
Subject Content
Probabilistic models and entropy
Inference in graphical models
Learning graphical models
Assessment
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.
| Type | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Intra-session Exam | 2 hours | 30 | N | Individual |
| Applied Project | 15 pages | 40 | N | Individual |
| Quiz | 6x40 min | 30 | N | Individual |
Teaching Periods
Autumn (2023)
Parramatta - Victoria Rd
On-site
Subject Contact Oliver Obst Opens in new window