Artificial Intelligence

Module Identifier
CS26520
Module Title
Artificial Intelligence
Academic Year
2027/2028
Semester
Semester 2
Co-ordinator
Dr Richard Jensen
Reading List
View on Aspire
Co-Requisite
CC21120 or CS21120
Other Staff
Dr Richard Jensen
Dr Myra Wilson

Assessment

Assessment Type

Assessment details

Proportion
Semester Assessment Blackboard test: 2 Hours 30%
Semester Exam Written exam: 2 Hours 70%
Supplementary Exam Blackboard test: 2 Hours 30%
Supplementary Exam Written exam: 2 Hours 70%

Learning Outcomes

  1. ​Write simple programs to solve problems using an AI technique discussed in the module.
  2. Demonstrate an understanding of the search techniques discussed in this module.
  3. Demonstrate an understanding of evolutionary algorithms and swarms.
  4. Describe the importance of propositional and predicate logic in Artificial Intelligence systems and solve simple problems.
  5. Apply data mining algorithms to data and interpret the results.
  6. Explain the function and use of fuzzy logic.

Brief description

This module begins with a motivational section on the foundations of AI and philosophical/ethical considerations before moving on to the main topics. These include knowledge representation, logic and fuzzy logic, data mining (focusing on classification and clustering), search (finding solutions to problems), and evolutionary algorithms and swarms.

Content

Introduction to AI
What is required to get computers to produce AI? Philosophical debate and ethical issues. Applications of AI.

Knowledge Representation and Logic
Symbolic and sub-symbolic representations, logical representation for problem solving.

Fuzzy Logic
Fuzzy sets and rules. Fuzzy inference.

Data Mining
Overview of data mining. KNN classification. K-means clustering. Hierarchical clustering.

Search
Searching for solutions using both uninformed and heuristic search.

Optimisation with Evolutionary Algorithms and Swarms
Evolutionary algorithms, selection mechanisms, variation operators. Swarm intelligence.

AI in Research
An invited speaker will talk about their current research in AI.

Revision
A recap of the material.

Module skills

Skills type

Skills details
Application of Number Many AI techniques require number application.
Communication Written skills needed for the exam.
Improving own Learning and Performance In-class test requires self-motivated study and work.
Information Technology Inherent in the topic.
Personal Development and Career planning Will feed into students' future career plans.
Problem solving In-class test and exam promotes and assesses this.
Research skills Assessing AI techniques for use in the assessments requires reading and researching other materials.
Subject Specific Skills AI techniques

Notes

This module is at CQFW Level 5