COMP30030 Introduction to Artificial Intelligence

Academic Year 2022/2023

This module offers a very broad introduction to the fundamental concepts, and algorithms behind artificial intelligence, and aims to provide the student with the ability to apply some of the basic techniques used in Artificial Intelligence (AI). It also aims to introduce students to some of the AI Frameworks that are currently popular. 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, Computer Vision and NLP, and Recommender Systems. Please note any student taking this module must have their own laptop. In addition, it is important that they can already programme in Java, and have previously taken modules covering the following topics: data structures, propositional logic, algebra and calculus.

PLEASE NOTE: In light of the current Covid situation, some changes have been made to the syllabus and the way the content is delivered and assessed. This is especially important for any students repeating the module.

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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) Demonstrate an understanding of the application of artificial intelligence techniques to game playing.
(4) Define the concepts of different planning systems and explain how they differ from classical search techniques.
(5) Have a basic understanding of different machine learning approaches, neural networks and genetic algorithms, and recommendation techniques.
(6) Demonstrate 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
Incompatibles:
COMP47460 - Machine Learning (Blended Del), COMP47750 - Machine Learning with Python, IS10060 - Digital Technology


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Written Exam Week 12 n/a Graded No

35
Class Test: Online Exam Unspecified n/a Graded No

15
Continuous Assessment: Assignment Sheets & Implementation Tasks Varies over the Trimester n/a Graded No

50

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.