MMath Mathematics and Statistics

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, COMP34111 (AI and Games), COMP34212 (Cognitive Robotics), COMP34812 (Natural Language Understanding) and COMP37212 (Computer Vision).

COMP11120 (for CS students) and MATH11121 or equivalent (for CM students). COMP13212 is a pre-requisite for all.

COMP11120 (for CS students) and MATH11121 or equivalent (for CM students). COMP13212 is a pre-requisite for all.

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.  

ILO 1: apply language models and information extraction techniques to solve problems in AI.

ILO 2: apply first-order logic to model physical situations and to reason about the effects of actions.

ILO 3: develop formal ontologies to represent knowledge in different domains.

ILO 4: implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.

ILO 5: solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.

ILO 6: select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations

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

Exam and assessments

1. Russell, Stuart J. (2022). Artificial intelligence: a modern approach. Pearson. ISBN: 9781292401133
    
2. Prince, Simon J. D. (2012). Computer vision: models, learning, and inference. Cambridge University Press. ISBN: 1107011795
    
3. Szeliski, Richard. (2022). Computer vision: algorithms and applications. Springer. ISBN: 9783030343729
    
4. Ben-Ari, Mordechai. Mathematical Logic for Computer Science. Springer London. ISBN: 1447141288 

Please note: AI forms part of the assessment method for this course unit. This unit is an introduction to AI and various AI techniques are implemented for coursework. (Category D: AI-integrated). For more information, please see: AI in Teaching and Learning Policy.