Fundamentals of Artificial Intelligence
|Unit level:||Level 1|
|Teaching period(s):||Semester 2|
|Offered by||School of Computer Science|
|Available as a free choice unit?:||Y
- COMP16121 - Object Oriented Programming with Java 1 (Compulsory)
Additional RequirementsStudents who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.
The course introduces the study of Artificial Intelligence (AI) for students in all course streams. It is designed to stand alone as an introduction to AI, but also to provide a background for more advanced study. The course presents AI from a probabilistic viewpoint, and is centred around two specific problems: (i) robot localization; (ii) speech understanding. The lectures will present the main theoretical ideas needed to tackle these problems; the examples classes will re-inforce these through paper-and-pencil exercises, and the labs will involve the development of programs to solve them. There will be one hour of lectures and one hour of examples classes each week, as well as five two-hour lab sessions over the semester.
The course teaches some of the fundamental techniques used currently in Artificial Intelligence: primarily how to represent knowledge and recognise patters in a probabilistic fashion.
Teaching and learning methods
11 in total, 1 per week
11 in total, 1 per week
10 hours in total, 5 2-hour sessions
Learning outcomes are detailed on the COMP14112 course unit syllabus page on the School of Computer Science's website for current students.
- Analytical skills
- Project management
- Oral communication
- Problem solving
- Written exam - 65%
- Written assignment (inc essay) - 10%
- Practical skills assessment - 25%
- Overview of Artificial Intelligence (1)
- Probability and the problem of robot localization (2)
- Foundations and limitations of probabilistic reasoning (1)
- Catch-up/revision (1)
- Introduction to speech processing and recognition (1)
- Feature extraction for speech and building a simple feature-based classifier (1)
- Introduction to Hidden Markov models (1)
- Inference, learning and classification with hidden Markov models (2)
- The role of probabilistic and non-probabilistic reasoning in other AI applications (1)
Examples classes will mirror the syllabus Laboratory exercises:
- 1.1 Robot localization
- 1.2 Robot localization
- 2.1 Speech processing
- 2.2 Speech processing
- 2.3D Speech processing
COMP14112 reading list can be found on the School of Computer Science website for current students.
Coursework is marked during laboratory sessions by demonstrators in person.
- Assessment written exam - 2 hours
- Lectures - 11 hours
- Practical classes & workshops - 21 hours
- Independent study hours - 66 hours