Module Information

Module Identifier
CS26520
Module Title
Artificial Intelligence
Academic Year
2021/2022
Co-ordinator
Semester
Semester 2
Co-Requisite
Reading List
Other Staff

Course Delivery

 

Assessment

Due to Covid-19 students should refer to the module Blackboard pages for assessment details

Assessment Type Assessment length / details Proportion
Semester Assessment Written Assignment:  30 hours  30%
Semester Exam 2 Hours   Written Exam  70%
Supplementary Assessment Written Assignment:  Resubmission of failed/nonsubmitted coursework components or ones of equivalent value.  30%
Supplementary Exam 2 Hours   Written Exam  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. The key concept of knowledge representation is introduced, followed by a variety of representational techniques including logic and fuzzy logic. Data mining follows this. It then focuses on the foundational issue of search (finding solutions to problems and games), naturally followed by evolutionary algorithms, optimisation and swarms.

Content

1. Introduction to AI
What is required to get computers to produce AI? Philosophical debate and ethical issues. Applications of AI such as games, bioinformatics, business, robotics.

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

3. Fuzzy Logic
Fuzzy sets and rules. Fuzzy inference.

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

5. Search
Searching for solutions using both uninformed and heuristic search.

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

7. AI in Research

8. Revision

Module Skills

Skills Type Skills details
Application of Number Many AI techniques require number application.
Communication Written skills needed for the written assessment.
Improving own Learning and Performance Written assignment 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 Written assignment promotes and assesses this.
Research skills Assessing AI techniques for use in the written assignments requires reading and researching other materials.
Subject Specific Skills AI techniques
Team work

Notes

This module is at CQFW Level 5