COMP14112 Fundamentals of Artificial Intelligence syllabus 2017-2018
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.
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.
- 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
11 in total, 1 per week
11 in total, 1 per week
10 hours in total, 5 2-hour sessions
Coursework is marked during laboratory sessions by demonstrators in person.
- Assessment written exam (2 hours)
- Lectures (11 hours)
- Practical classes & workshops (21 hours)
- Analytical skills
- Project management
- Oral communication
- Problem solving
|Programme outcome||Unit learning outcomes||Assessment|
|A4 A5 D6||Understand in broad outline the principal challenges of AI, the major research areas and the overall historical development of the subject.|
|A1 A2 A5 D6||Understand the fundamentals of probability theory, considered as an account of reasoning under uncertainty, and its central role in AI.|
|A1 A5 B1 B3 D6||Understand in detail the problem of robot localization, and the methods used to solve it.|
|A1 A5 B1 B3 D6||Understand in detail the problem of speech recognition, and the methods used to solve it.|
|A1 A5 B1 B3 C5 D4 D5 D6||Develop practical expertise in implementing probabilistic algorithms in AI.|
|A4 A5 D6||Understand the philosophical foundations and limitations of probability theory in AI.|
COMP14112 does not have a specified reading list.
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.