Modules

Core Module Information
Module title: Artificial Intelligence
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET11125
Module leader: Sarah L. Thomson
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (3rd edition) by Russell and Norvig. The indicative content from the book is as follows: Introduction: What is AI? History of AI and the state of the art. Agents : An introduction to agents, their behaviors and structure Searching: Problem solving by searching, heuristics, local search and optimisation and adversarial search Constraint satisfaction problems: defining and solving CSPs Logic: Propositional logic, first-order logic, knowledge representation In addition to this the module will feature introductions to other AI techniques including neural networks, machine learning and nature inspired methods.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Critically reflect on how AI concepts can underpin problem solving tasks.

LO2: Critically Analyse and apply the fundamental concepts and origins of artificial intelligence to solve example problems

LO3: Choose, compare and implement AI based solutions to problem solving tasks making use of appropriate software tools and libraries.

LO4: Design and execute AI-based experiments to address example problems, showcasing proficiency using various AI techniques.

LO5: Critically assess the effectiveness and appropriateness of specific AI techniques for specific applications.
Full Details of Teaching and Assessment
2025/6, Trimester 1, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Sarah L. Thomson
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 10 There will be one hour-long lecture per week explaining key concepts.
Face To Face Practical classes and workshops 20 Practical classes and workshops. You will undertake computer based practical learning exercises and answer tutorial questions, mainly through discussion in groups
Online Guided independent study 170 Self-learning materials, including academic papers, book chapters, lecture notes, tutorials, blogs, videos, and industry reports, will be made available on the Moodle page. Lecture slides and coding notebooks will also be released one week before the lecture session.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Practical Skills Assessment 40 2~3 Week 10 HOURS= 1000 words Practical Skills Assessment. The assessment is split into two main parts - each providing practical tasks to demonstrate the application of AI techniques to solve problems making appropriate choices in doing so. These assessments will vary from year to year but will be founded in: (1) LLM prompt construction, tuning and optimisation (2) classical search techniques and comparisons.
Class Test 10 1~2~3~4~5 Week 11 HOURS= 10 Ten end of unit tests. Each has ten multiple choice questions. Each test is about the material is the unit just covered.
Class Test 50 1~2~3~4~5 Exam Period HOURS= 2 hours Approximately 25 multiple choice questions and a choice of one out of two questions composed of short answer questions
Component 1 subtotal: 50
Component 2 subtotal: 50
Module subtotal: 100

Indicative References and Reading List - URL:
Artificial Intelligence
Artificial Intelligence
 

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