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COMP30030

Academic Year 2024/2025

Introduction to Artificial Intelligence (COMP30030)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Lorraine McGinty
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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 .

About this Module

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
Autonomous Student Learning

60

Lectures

24

Practical

24

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Written exam-paper End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No
45
No
Quizzes/Short Exercises: 3 Class Quizzes Week 4, Week 7, Week 12 Alternative linear conversion grade scale 40% No
45
No
Assignment(Including Essay): Homework assignments Week 3, Week 5, Week 6, Week 8, Week 9, Week 10 Alternative linear conversion grade scale 40% No
10
No

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

• Group/class feedback, post-assessment
• Online automated feedback

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.

Name Role
Mr Jiwei Zhang Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 11:00 - 11:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 11:00 - 11:50
Autumn Practical Offering 1 Week(s) - Autumn: Weeks 2-12 Fri 10:00 - 11:50
Autumn Practical Offering 2 Week(s) - Autumn: Weeks 2-12 Fri 10:00 - 11:50