Core Module Information
Module title: Artificial Intelligence

SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET09122
Module leader: Ben Paechter
School School of Computing
Subject area group: Software Engineering
Prerequisites

Module Code: SET08122
Examples of Equivalent Learning Industry implementation of equivalent algorithms and data structures. Learning involving programming of non-trivial algorithms and data structures.

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:

LO1: Critically reflect on how AI concepts can underpin problem solving tasks
LO2: 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: Solve example problems using AI techniques
LO5: Evaluate the effectiveness and appropriateness of specific AI techniques for specific applications

Full Details of Teaching and Assessment
2020/1, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Ben Paechter
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
This module will be delivered using a combination of lectures and practicals and tutorials. The practicals offer the students a
chance to utilise tools and software libraries to experiment with
the AI techniques being covered in the lectures (LOs 3,4 and 5). The lecture material will concentrate on LOs 1,2 and 4, with
some coverage of LOs 3 and 5.
Embedding of employability/PDP/scholarship skills
The AI techniques to be covered are widely used in industry and academia.
The AI tools and techniques used within the module are recognised internationally.

Formative Assessment:
To Support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved

Summative Assessment:
Summative assessment is comprised of two components. The first component, which covers all learning outcomes (worth 40%) is itself comprised of end of section quizzes (10%) and end of module class test (30%). The second component (60%) also covers all learning outcomes. It is comprised of two lab reports (30% each) which will contain a combination of research, analysis, evaluation and implementation of AI solutions to simple problems.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Online Tutorial 12
Face To Face Practical classes and workshops 24
Face To Face Centrally Time Tabled Examination 2
Independent Learning Guided independent study 138
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Practical Skills Assessment 50 1,3,4,5 10 HOURS= 16, WORDS= 0
Centrally Time Tabled Examination 50 1,2,4 14/15 HOURS= 2, WORDS= 0
Component 1 subtotal: 50
Component 2 subtotal: 50
Module subtotal: 100
2020/1, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: 002
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Ben Paechter
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
This module will be delivered using a combination of lectures and practicals and tutorials. The practicals offer the students a
chance to utilise tools and software libraries to experiment with
the AI techniques being covered in the lectures (LOs 3,4 and 5). The lecture material will concentrate on LOs 1,2 and 4, with
some coverage of LOs 3 and 5.
Embedding of employability/PDP/scholarship skills
The AI techniques to be covered are widely used in industry and academia.
The AI tools and techniques used within the module are recognised internationally.

Formative Assessment:
To Support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved

Summative Assessment:
Summative assessment will be in the form of four parts totalling 100% of the final mark:
There will be a series of end-of-section online quizzes that will provide formative assessment throughout the course. These quizzes form 10% of the overall assessment (covering LOs 1 - 5). Two artificial intelligence tasks and associated lab reports will each be worth 30% of the final mark (covering LOs 1 - 5). The assignments will be submitted in weeks 10 and 13. At the end of the module an online class test (covering LOs 1 - 5) will be undertaken for the remaining 30% of the final mark.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Online Tutorial 12
Face To Face Practical classes and workshops 24
Face To Face Centrally Timetabled (Digital) Exam 4.30
Independent Learning Guided independent study 138
Total Study Hours202
Expected Total Study Hours for Module202


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Class Test 10 1,2,3,4,5 12 HOURS= 02.30, WORDS= 0
Class Test 30 1,2,3,4,5 13 HOURS= 02.00, WORDS= 0
Laboratory report 30 1,2,3,4,5 10
Laboratory report 30 1,2,3,4,5 13
Component 1 subtotal: 40
Component 2 subtotal: 60
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader