Artificial Intelligence - COM00001I

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  • Department: Computer Science
  • Module co-ordinator: Dr. Suresh Manandhar
  • Credit value: 20 credits
  • Credit level: I
  • Academic year of delivery: 2017-18

Related modules

Prohibited combinations

  • None

Module occurrences

Occurrence Teaching cycle
A Spring Term 2017-18 to Summer Term 2017-18

Module aims

The module will introduce the field of artificial intelligence and study the principal ideas and techniques in three core topic areas: problem solving, knowledge representation and machine learning. It will provide students with the foundations that are necessary to study the more advanced, specialized artificial intelligence modules that are offered in the third and fourth years as well provide support for the Vision and Graphics module offered in the second year.

The module will cover the following topics: Problem Solving (Problem Representation, Uninformed and Informed Search), Knowledge Representation (Logic-based languages, e.g. Description Logic, and Ontologies and the Semantic web), Machine Learning (Symbolic learning, Decision Trees, Bayesian Learning, MLE and MAP).

In each of the three topic areas the module will aim to give students hands-on experience with tools and techniques illustrating principles that can be exploited in a wide range of areas outside of artificial intelligence.

Module learning outcomes

The students should be familiar with a number of concepts central to AI, and be able to apply some of the main tools and techniques in the field on a range of tasks. These will come from the three general areas of (1) Problem Solving, (2) Machine Learning, and (3) Knowledge Representation. The students should also acquire hands-on experience with using state-of-the-art methods and tools to tackle non-toy problems in each of these three areas.

Assessment

Task Length % of module mark
Practical
Practical
N/A 30
University - closed examination
Artificial Intelligence (ARIN)
3 hours 70

Special assessment rules

None

Reassessment

Task Length % of module mark
Practical
Practical
N/A 30
University - closed examination
Artificial Intelligence (ARIN)
3 hours 70

Module feedback

Feedback will be provided following the Department’s online feedback system for open assessments.

Key texts

**** Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 3rd Edition

**** Bob DuCharme, Learning SPARQL (2nd ed.), O'Reilly, 2013

*** Tom Mitchell, Machine Learning, McGraw Hill, 1997



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.