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COMP14112: Fundamentals of Artificial Intelligence (2012-2013)

This is an archived syllabus from 2012-2013

Fundamentals of Artificial Intelligence
Level: 1
Credit rating: 10
Pre-requisites: COMP16121
Co-requisites: COMP11120
Duration: 11 weeks in second semester
Lectures: 11 in total, 1 per week
Examples classes: 11 in total, 1 per week
Labs: 10 hours in total, 5 2-hour sessions
Course Leader: Tim Morris
Additional Lecturers: Xiao-Jun Zeng
Course leader: Tim Morris

Additional staff: view all staff
Sem 2 Lecture 1.1 Fri 11:00 - 12:00 -
Sem 2 w2+ Examples LF15 Thu 12:00 - 13:00 Z
Sem 2 w2+ Examples LF15 Tue 12:00 - 13:00 M+W
Sem 2 w2+ Examples LF15 Thu 13:00 - 14:00 B+X
Sem 2 w2+ Examples LF15 Fri 13:00 - 14:00 Y
Sem 2 A w3+ Lab LF31 Tue 13:00 - 15:00 Z
Sem 2 A w3+ Lab LF31 Mon 13:00 - 15:00 Y
Sem 2 A w3+ Lab LF31 Tue 15:00 - 17:00 M+W
Sem 2 A w3+ Lab LF31 Mon 15:00 - 17:00 B+X
Assessment Breakdown
Exam: 65%
Coursework: 10%
Lab: 25%


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.

Programme outcomeUnit learning outcomesAssessment
A4 A5 D6Understand in broad outline the principal challenges of AI, the major research areas and the overall historical development of the subject.
  • Examination
A1 A2 A5 D6Understand the fundamentals of probability theory, considered as an account of reasoning under uncertainty, and its central role in AI.
  • Examination
A1 A5 B1 B3 D6Understand in detail the problem of robot localization, and the methods used to solve it.
  • Examination
A1 A5 B1 B3 D6Understand in detail the problem of speech recognition, and the methods used to solve it.
  • Examination
A1 A5 B1 B3 C5 D4 D5 D6Develop practical expertise in implementing probabilistic algorithms in AI.
  • Lab assessment
A4 A5 D6Understand the philosophical foundations and limitations of probability theory in AI.
  • Examination


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

Reading List

Course notes will be distributed in the lectures and made available online. There is no recommended text book.