University of St Andrews

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: 2

Planned timetable: TBC

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.

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

Assessment pattern

As used by St Andrews: Coursework - 60%, Exam - 40%


Re-assessment: Coursework - 60%, Exam - 40%

Personnel

Module coordinator: Dr K Terzic
Module teaching staff: Dr Lei Fang, Dr Nguyen Dang
Module coordinator email kt54@st-andrews.ac.uk

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