Skip to navigation | Skip to main content | Skip to footer
Menu
Menu

COMP14112 Fundamentals of Artificial Intelligence syllabus 2019-2020

COMP14112 materials

COMP14112 Fundamentals of Artificial Intelligence

Level 1
Credits: 10
Enrolled students: pending

Course lecturer: not assignedAdditional staff: view all staff

Requisites

  • 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

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 (11 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

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.