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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


  • Pre-Requisite (Compulsory): COMP16121
  • Co-Requisite (Compulsory): COMP11120

Assessment methods

  • 65% Written exam
  • 10% Coursework
  • 25% Practical skills assessment
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


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

Teaching methods


11 in total, 1 per week

Examples classes

11 in total, 1 per week


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.