ÐÓ°ÉÂÛ̳

CS5016 Uncertainty in Artificial Intelligence

Academic year

2025 to 2026 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

TBC

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr K Terzic

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Lei Fang, Dr Nguyen Dang

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

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

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

123

The number of hours that students are expected to invest in independent study over the period of the module.

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