SIT743 - Bayesian Learning and Graphical Models
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | SIT718 or SIT741 |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Study commitment | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - campus | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week, weekly meetings. |
| Scheduled learning activities - online | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour practical experience (workshop) per week, weekly meetings. |
Content
This unit provides the opportunity for students to equip themselves with a strong background in the analysis of data using Bayesian and Graphical models. Students will be able to model, analyse and extract complex patterns from multivariate, correlated datasets and apply their learning in data science, machine learning and data mining tasks. Concepts such as probability theory, Bayesian modelling and probabilistic graphical models form the core knowledge of this unit.
Unit Fee Information
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