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COMP14112 Fundamentals of Artificial Intelligence syllabus 2018-2019

COMP14112 materials

COMP14112 Fundamentals of Artificial Intelligence

Level 1
Credits: 10
Enrolled students: 245

Course leader: Tim Morris

Additional staff: view all staff


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

Additional requirements

  • Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

Assessment methods

  • 65% Written exam
  • 10% Coursework
  • 25% Practical skills assessment
Sem 2 Lecture 1.1 Thu 15:00 - 16:00 -
Sem 2 w2+ Examples Collab 1 Thu 11:00 - 12:00 Y
Sem 2 w2+ Examples IT407 Fri 11:00 - 12:00 Z
Sem 2 w2+ Examples IT407 Fri 12:00 - 13:00 X
Sem 2 w2+ Examples Collab 1 Tue 15:00 - 16:00 M+W
Sem 2 A w3+ Lab LF31 Wed 09:00 - 11:00 Z
Sem 2 A w3+ Lab LF31 Tue 11:00 - 13:00 X
Sem 2 A w3+ Lab LF31 Mon 16:00 - 18:00 Y
Sem 2 A w3+ Lab LF31 Tue 16:00 - 18:00 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 (11 hours)
  • Practical classes & workshops (21 hours)

Employability skills

  • Analytical skills
  • Innovation/creativity
  • Project management
  • Oral communication
  • Problem solving

Learning outcomes

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

Reading list

COMP14112 does not have a specified reading list.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.