CS5016 Uncertainty in Artificial Intelligence
Academic year
2025 to 2026 Semester 2
Curricular information may be subject to change
Further information on which modules are specific to your programme.
Key module information
SCOTCAT credits
15
SCQF level
SCQF level 11
Planned timetable
TBC
Module coordinator
Dr K Terzic
Module Staff
Dr Lei Fang, Dr Nguyen Dang
Module description
This module covers reasoning and decision making in the presence of uncertainty. It introduces probabilities and probabilistic reasoning, approximate inference (Monte Carlo methods), Bayesian Networks and different types of Markov models. Students will learn the relevant theoretical concepts and gain practical experience in developing solutions to real problems.
Assessment pattern
Coursework - 60%, Exam - 40%
Re-assessment
Coursework - 60%, Exam - 40%
Learning and teaching methods and delivery
Weekly contact
2hr x 11 weeks lectures, 1hr x 5 weeks tutorial/discussion
Scheduled learning hours
27
Guided independent study hours
123
Intended learning outcomes
- Understand the principles of probabilistic reasoning in Artificial Intelligence
- Understand the role of approximate inference in probabilistic reasoning
- Be able to model and solve problems using Bayesian Networks
- Understand and apply Markov models to AI problems