COMP30030 Introduction to Artificial Intelligence

Academic Year 2024/2025

This module offers a broad introduction to the fundamental concepts and algorithms behind Artificial Intelligence (AI), and aims to provide the student with the ability to apply some of the basic techniques used in AI. Some of the module topics covered include: Knowledge Representation, Problem Solving & Search, Game Playing, Optimisation Problems, Planning, Machine Learning and Classification, Genetic Algorithms, Neural Networks, Deep Learning and Computer Vision.

Please note any student taking this module must have their own laptop. In addition, it is important that they have a knowledge of programming, and have previously taken modules covering the following topics: data structures, propositional logic, and algebra .

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Curricular information is subject to change

Learning Outcomes:

By the end of this module a student should be able to:
(1) Explain the underlying principles, and evaluate the advantages and limitations, of the AI approach to problem solving.
(2) Describe and implement a range of search algorithms and discuss the limitations associated with each.
(3) Apply some of the basic adversarial game playing algorithms and techniques that can be used to improve their performance characteristics. 

(4) Compare and contrast alternative AI algorithms often used to solve Optimization Problems, and demonstrate that they can practically apply AI techniques such as Simulated Annealing and Genetic Algorithms.
(5) Understand what is meant by AI Planning, and show how they can represent problems using a suitable planning representation, and be capable of applying a planning algorithm to ultimately achieve a total order plan.
(6) Understand the difference between supervised and unsupervised learning Machine Learning techniques such as, Decision Trees, Naïve Bayes, kNN, K-Means, and Association Rule Mining, and describe their limitations.
(7) Distinguish between different types of Neural Networks in terms of the data they assume and the problems they are used to solve. A deeper discussion around Computer Vision and CNN Architectures is covered and students are expected to be able to carry out the various steps/calculations that are relevant here. 

(8) Show that they have researched the module content beyond lectures.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Practical

24
Autonomous Student Learning

60
Total

108
Approaches to Teaching and Learning:
Teaching and learning approaches include: active/task-based learning; lectures; lab work; 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Students should have a solid knowledge of Data Structures and Algorithms and reasonable programming skills (pref Java).


Module Requisites and Incompatibles
Pre-requisite:
COMP20280 - Data Structures, COMP20290 - Algorithms

Incompatibles:
COMP47460 - Machine Learning (Blended Del), COMP47750 - Machine Learning with Python


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment(Including Essay): Homework assignments n/a Alternative linear conversion grade scale 40% No

15
Exam (In-person): Written exam-paper n/a Alternative linear conversion grade scale 40% No

40
Quizzes/Short Exercises: 3 Class Quizzes n/a Alternative linear conversion grade scale 40% No

45

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

Feedback will be given to individual students for assignments they submit in this module. Online tests are corrected automatically and a student will see their grade once they submit an online test.