COMP24011 Introduction to AI syllabus 2021-2022

COMP24011 Introduction to AI

Level 2
Credits: 10
Enrolled students: pending

Course leader: Ian Pratt-Hartmann

Additional staff: view all staff

Requisites

• Pre-Requisite (Compulsory): COMP11120
• Pre-Requisite (Compulsory): COMP13212

Additional requirements

• COMP11120 (not a pre-requisite for CM)

Assessment methods

• 80% Written exam
• 20% Coursework

Timetable

Semester	Event	Location	Day	Time	Group
Sem 1 w1-5,7-12	ONLINE Lecture		Tue	09:00 - 10:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Mon	09:00 - 11:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Wed	09:00 - 11:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Thu	09:00 - 11:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Fri	11:00 - 13:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Tue	11:00 - 13:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Thu	13:00 - 15:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Tue	13:00 - 15:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Mon	16:00 - 18:00	-
Sem 1 w2,4,7,9,11-12	Lab	*SD 1.8+1.10	Thu	16:00 - 18:00	-

Overview

The Unit constitutes an introduction to the field of Artificial Intelligence, aiming at once to give a broad overview of the subject and to serve as a basis for more detailed third year courses, particularly, COMP34120 (AI and Games), COMP34212 (Cognitive Robotics), COMP34412 (Natural Language Systems) and COMP37212 (Computer Vision).

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.

Aims

A student completing this course should:

• be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;
• be able to apply first-order logic to model physical situations and reason about the effects of actions,
• to appreciate the limitations of logic and to select appropriate responses to these limitations;
• be able to develop formal ontologies to represent knowledge in different domains;
• be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;
• be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;
• be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;
• have an appreciation of the central philosophical problems connected with artificial intelligence.

Syllabus

Teaching methods

2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)

Feedback methods

Exam and assessments

Study hours

• Lectures (22 hours)
• Practical classes & workshops (8 hours)

Learning outcomes

On successful completion of this unit, a student will be able to:

• ILO 1 To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.
• ILO 2 To be able to apply first-order logic to model physical situations and to reason about the effects of actions.
• ILO 3 To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.
• ILO 4 To be able to develop formal ontologies to represent knowledge in different domains.
• ILO 5 To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.
• ILO 6 To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.
• ILO 7 To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.

Reading list