This is an archived syllabus from 2013-2014
COMP14112 Fundamentals of Artificial Intelligence syllabus 2013-2014
COMP14112 Fundamentals of Artificial Intelligence
Level 1
Credits: 10
Enrolled students: 215
Course leader: Tim Morris
Additional staff: view all staff
Requisites
- Pre-Requisite (Compulsory): COMP16121
- Co-Requisite (Compulsory): COMP11120
Assessment methods
- 65% Written exam
- 10% Coursework
- 25% Practical skills assessment
| Semester | Event | Location | Day | Time | Group |
|---|---|---|---|---|---|
| Sem 2 | Lecture | 1.1 | Fri | 11:00 - 11:00 | - |
| Sem 2 w2+ | Examples | LF15 | Thu | 12:00 - 12:00 | Z |
| Sem 2 w2+ | Examples | LF15 | Tue | 12:00 - 12:00 | M+W |
| Sem 2 w2+ | Examples | LF15 | Mon | 12:00 - 12:00 | B+X |
| Sem 2 w2+ | Examples | LF15 | Fri | 13:00 - 13:00 | Y |
| Sem 2 A w3+ | Lab | LF31 | Mon | 13:00 - 13:00 | Y |
| Sem 2 A w3+ | Lab | LF31 | Tue | 13:00 - 13:00 | Z |
| Sem 2 A w3+ | Lab | LF31 | Mon | 15:00 - 15:00 | B+X |
| Sem 2 A w3+ | Lab | LF31 | Tue | 15:00 - 15:00 | M+W |
Overview
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.Aims
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.
Syllabus
- 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
Teaching methods
Lectures
11 in total, 1 per week
Examples classes
11 in total, 1 per week
Laboratories
10 hours in total, 5 2-hour sessions
Feedback methods
Coursework is marked during laboratory sessions by demonstrators in person.Study hours
- Assessment written exam (2 hours)
- Lectures (14 hours)
- Practical classes & workshops (21 hours)
Employability skills
- Analytical skills
- Innovation/creativity
- Project management
- Oral communication
- Problem solving
Learning outcomes
On successful completion of this unit, a student will be able to:
Learning outcomes are detailed on the COMP14112 course unit syllabus page on the School of Computer Science's website for current students.
Reading list
No reading list found for COMP14112.
Additional notes
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.