Unit name | Artificial Intelligence (Teaching Unit) |
---|---|
Unit code | COMS30014 |
Credit points | 0 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Ray |
Open unit status | Not open |
Pre-requisites |
COMS10016 Imperative and Functional Programming and COMS10017 Object-Oriented Programming and Algorithms I or equivalent COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent COMS20011 Data-Driven Computer Science or equivalent Programming paradigms, mathematics (including statistics, probability and algebra), and also desirable basic ideas of data mining/analysis |
Co-requisites |
EITHER Assessment Unit COMS30013 Artificial Intelligence (10 credit examination assessment) OR COMS30012 Artificial Intelligence (20 credit coursework assessment). Please note: COMS30014 is the Teaching Unit for the Artificial Intelligence option. Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMS30013) or coursework (20 credits, COMS30012) by selecting the appropriate co-requisite assessment unit. Any other students that are permitted to take the Artificial Intelligence option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30013). |
School/department | School of Computer Science |
Faculty | Faculty of Engineering |
Artificial Intelligence (AI) systems and tools are virtually everywhere around us at present, no longer being just ‘science fiction’. Since Alan Turing, considered as the father of AI, postulated the question “can machines think?”, the world has witnessed innumerable advances in the field. “Thinking machines” are continuously developed worldwide to contribute to the societal good, in many aspects and sectors like economy, sustainability, safety, fairness, education, health, manufacturing and entertainment, to name a few. But, what are the foundations behind these “thinking machines” and intelligent tools?
This unit introduces the field of AI and its foundational principles, techniques and algorithms. It firstly covers the basics of knowledge representation and reasoning, followed by AI methods for search and optimisation. These foundations are then used in the second half of the unit, where the paradigm of intelligent agents, multi-agent systems and automated planning techniques are covered.
We will introduce and explore the main paradigms behind AI:
We will also apply the above paradigms to define AI agents or teams of them to solve challenging real-world tasks or complex problem-solving games that would normally require capabilities resembling human intelligence.
Successful completion of the unit will enable students to:
When assessed by Examination, in addition to the general ILOs above, the student will be also able to:
OR
When assessed by Coursework, in addition to the general ILOs above, the student will be also able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.
Teaching will take place over Weeks 1-7, with consolidation and revision sessions in Weeks 11 and 12 for students being assessed by examination.
Examination details:
January Timed assessment (100%, 10 credits)
OR
Coursework details:
Coursework (100%) - to be completed during weeks 8-10.