School of Computer Science

Module 06-27112 (2017)

MSc Introduction to Artificial Intelligence

Level 4/M

Claudio Zito Semester 2 10 credits
Co-ordinator: Claudio Zito
Reviewer: Nicholas Hawes

The Module Description is a strict subset of this Syllabus Page.

Outline

This module provides a general introduction to artificial intelligence, its techniques, and main subfields. The principal focus of the module will be on the common underlying principles, such as knowledge representation, search, and learning.


Aims

The aims of this module are to:

  • Provide a general introduction to artificial intelligence, its techniques and its main subfields.
  • Give an overview of key underlying ideas, such as knowledge representation, reasoning, search, and learning.
  • Demonstrate the need for different approaches for different problems

Learning Outcomes

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

  1. Discuss the major issues and techniques in a variety of sub-fields of AI, such as vision, robotics, natural language processing, planning, probabilistic reasoning, and machine learning.
  2. Apply a variety of standard AI techniques to simple examples.
  3. Understand applications of AI to real world situations and possible problems and limitations.

Restrictions

None


Taught with

  • 06-27110 - ICY Introduction to Artificial Intelligence

Teaching methods

Large-Group Lectures and Lab sessions in Computer Science Labs

Contact Hours:

39


Assessment

Sessional: 1.5 hr Examination (70%) Continuous Assessment (30%)

Supplementary (where allowed): 1.5 hr Examination (100%)


Detailed Syllabus

  1. Overview of AI terms and concepts
  2. Rational Agents
  3. Search Algorithms
  4. Optimality and Heuristics in Search
  5. Game Algorithms
  6. Probability Theory
  7. Monte Carlo Methods
  8. Belief Networks and Bayesian Inference
  9. Markov Chains and Decision Processess
  10. Reinforcement Learning

Programmes containing this module