SIT743 - Bayesian Learning and Graphical Models
| Year: | 2023 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. |
| Scheduled learning activities - campus | 1 x 2 hour online class per week, 1 x 2 hour workshop per week. weekly drop-in sessions. |
| Scheduled learning activities - cloud (online) | Online independent and collaborative learning including optional scheduled activities as detailed in the unit site. |
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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