2016/17 Undergraduate Module Catalogue

COMP2611 Artificial Intelligence

10 creditsClass Size: 150

Module manager: Dr John Stell
Email: j.g.stell@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2016/17

Pre-requisite qualifications

Either
COMP1421 Fundamental Mathematical Concepts
Or
ELEC1704 Further Engineering Mathematics

This module is not approved as a discovery module

Module summary

Artificial Intelligence is a rapidly evolving field. This module provides an introduction to a broad range of topics in Artificial Intelligence. You will learn how to design and implement simple intelligent agents; the principles of knowledge representation and inference, machine learning and explore application in areas such as text mining, image analysis and bio-inspired computing

Objectives

On completion of this module, students should be able to:
- understand the scope and nature of artificial intelligence in the discipline of computing;
- understand the fundamental ideas and techniques within the main approaches to artificial intelligence;
- implement a simple intelligent system by applying suitable
theoretical concepts from artificial intelligence;
- appreciate how complex real world processes (e.g. biology, language, speech, imaging, reasoning, etc.) can be analysed and modelled, and of the role of these techniques in developing artificial intelligence applications;
- understand the main concepts of text mining and corpus processing;
- understand the main concepts in statistical learning and optimization;
- understand the main concepts of knowledge representation and inference.

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-demonstrate a broad understanding of the concepts, information, practical competencies and techniques which are standard features in a range of aspects of the discipline;
-apply generic and subject specific intellectual qualities to standard situations outside the context in which they were originally studied;
-appreciate and employ the main methods of enquiry in the subject and critically evaluate the appropriateness of different methods of enquiry;
-use a range of techniques to initiate and undertake the analysis of data and information;
-adjust to professional and disciplinary boundaries;
-effectively communicate information, arguments and analysis in a variety of forms;

Syllabus

- Overview of AI, its scope, history, and prospects. Search techniques and problem solving (informed and uninformed search, heuristic search, genetic algorithms, game playing). Uncertainty (applications of probability, Bayesian networks, Hidden Markov Models). Basic concepts of machine learning.
- Corpora; web-as-corpus text capture, cleansing, tokenisation; type-token frequency distributions, Zipf's law; stemming, morphology, collocations, concordances;
- Topics in statistical learning.
- Knowledge Representation and Reasoning; First order predicate calculus; inference (including resolution); representing knowledge in logic.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Practical	9	1.00	18.00
Private study hours			60.00
Total Contact hours			40.00
Total hours (100hr per 10 credits)			100.00

Opportunities for Formative Feedback

Coursework and labs.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Practical	Lab Implementation	10.00
Report	Project Report	10.00
Total percentage (Assessment Coursework)		20.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr 00 mins	80.00
Total percentage (Assessment Exams)		80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 07/09/2016

Disclaimer

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