Computer Science and Mathematics (3 Years) [BSc]

Fundamentals of Artificial Intelligence


Unit code: COMP14112
Credit Rating: 10
Unit level: Level 1
Teaching period(s): Semester 2
Offered by School of Computer Science
Available as a free choice unit?: Y

Requisites

Co-Requisite
Prerequisite

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.

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.

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.

Teaching and learning 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

Learning outcomes

Learning outcomes are detailed on the COMP14112 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

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

Assessment methods

  • Written exam - 65%
  • Written assignment (inc essay) - 10%
  • Practical skills assessment - 25%

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

Recommended reading

COMP14112 reading list can be found on the School of Computer Science website for current students.

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
  • Independent study hours - 66 hours

Teaching staff

David Morris - Unit coordinator

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