Courses / Module

On successful completion of the module, students should be able to:

• Understand the concepts of prior and posterior distributions.
• Compare Bayesian and classical models.
• Implement Bayesian models and apply them to problems in data science.

• Tutorial & Labs/Practical hours are subject to review by the Head of Department.

Delivery methods	Hours
Lectures	24
Labs / Practicals	0
Tutorials	4
Planned learning activities	0
Independent student activities	97
Total	125

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded

Assessment type	Options	Weighting	Duration
Continuous Assessment		25%
University scheduled written examination	On-campus	75%	120 minutes
Other		0%
Total		100%	120 minutes

• Pass standard: 40%
• Could the result of this module be capped? No
• Penalties: Work submitted after the deadline will not be graded and will not count.

• Are supplemental/resit registrations permitted? Yes
• University scheduled written examination (Autumn): 120 minutes, On-campus

• ST201

• ST303

For a full timetable search, go to NUIM Timetable. Venue locations can be found here.