View Specification DocumentArtificial Intelligence (H) COMPSCI4004
- Academic Session: 2026-27
- School: School of Computing Science
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
Artificial Intelligence (H) is an introduction to Artificial Intelligence, giving the students an overview of intelligent agent design.
Timetable
3 hours per week
Requirements of Entry
Mandatory:
Level 1 mathematics or equivalent; working knowledge of matrices, linear spaces and basic geometry.
Fluency in a high-level programming language such as Java or Python
Data Fundamentals or equivalent; working knowledge of vector spaces, numerical analysis/optimisation and probability.
Recommended:
Machine Learning or equivalent.
Excluded Courses
Artificial Intelligence (M)
Co-requisites
None
Assessment
Examination 75%; Continuous assessment- practical exercises 25%.
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
Course Aims
The aim of this course is to provide an overview of intelligent agent design, where agents perceive their environment and act rationally to fulfil their goals. Students will gain practical experience in labs, programming various aspects of intelligent systems.
Intended Learning Outcomes of Course
By the end of the course students will be able to:
1. Demonstrate familiarity with the history of AI, philosophical debates, and understand the potential and limitations of the subject in its current form;
2. Explain the basic components of an intelligent agent, and be able to map these onto other advanced subjects such as information retrieval, computer vision, database systems, robotics, human-computer interaction, reactive systems etc
3. Discuss difficulties in computer perception;
4. Discuss basic issues in planning;
5. Explain and apply search-based problem-solving techniques;
6. Formulate and apply Bayesian networks in modelling and planning;
7. Explain and apply utility theory as a probabilistic framework for rational decision making;
8. Explain and apply basic machine learning techniques to learn from rewards and observations.