SIT743 - Bayesian Learning and Graphical Models
| Year: | 2025 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
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.