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Module Descriptor School of Computer Science and Statistics

Module CodeCS7IS2
Module NameArtificial Intelligence
Module Short Title
ECTS5
Semester TaughtMT
Contact Hours

2 Lecture hours per week

Module PersonnelDr. Annalina Caputo
Learning Outcomes

On successful completion of this module a student should be able to:

  • IS2LO1 Appreciate the scope, applications and limitations of artificial intelligence;
  • IS2LO2 Choose and use appropriate representations for various kinds of knowledge;
  • IS2LO3 Comprehend and apply search, reasoning and planning strategies;
  • IS2LO4 Develop intelligent systems that handle uncertainty;
  • IS2LO5 Apply knowledge representation, reasoning, and machine learning techniques to real-world problems in natural language processing, perception or robotics.
Learning Aims

This module aims to provide students with a thorough overview of the techniques that underlie intelligent systems and an ability to apply these techniques to real-world problems.

Module Content

Specific topics addressed in this module include:

  • Search;
  • Problem solving;
  • Knowledge and reasoning – representations, logic, reasoning;
  • Classical automated planning;
  • Representing and reasoning with uncertainty;
  • Learning;
  • Introductions to topics in Natural Language Processing, Perception, Robotics.
Recommended Reading List

Stuart Russell and Peter Norvig (2010) Artificial Intelligence: A Modern Approach, Third Edition. Upper Saddle River (NJ): Prentice Hall.

Module Prerequisites

CS7CS4 or CS4404 Machine Learning

Assessment Details

Coursework: 50%

Exam: 50%

Assessment is by written exam (50%) and coursework (50%).

Assessment in the Supplemental session will be based on 100% exam.

Assignments will provide practical experience, in both theory and programming.

Module Website
Academic Year of Data2017/18