Introduction to Artificial Intelligence - COMP5280

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Canterbury
Autumn Term 5 15 (7.5) Palaniappan Ramaswamy checkmark-circle

Overview

This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. You learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.

Details

Contact hours

Total contact hours: 22
Private study hours:128
Total study hours: 150

Availability

Autumn or Spring

Method of assessment

13.1 Main assessment methods
A1 – Practical assignment (25%)
A2 – Practical assignment (25%)
2 hour unseen written examination (50%)

13.2 Reassessment methods
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Indicative reading

S.J. Russell & P. Norvig, "Artificial Intelligence: a modern approach", 2nd Edition. Prentice-Hall, 2002. (main textbook)
S. Pinker. "How the Mind Works", W.W. Norton & Company, 1999.
A. Cawsey, "The Essence of Artificial Intelligence", Prentice-Hall, 1998.
P. Bentley. "Digital Biology", Simon & Schuster, 2002

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1. Explain the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature of intelligence and learning.
2. Describe the main kinds of state-space search algorithms, discussing their strengths and limitations.
3. Explain the main concepts and principles associated with different kinds of knowledge representation, such as logic, case-based representations, and subsymbolic/connectionist representations.
4. Explain the differences between the major kinds of machine learning problems – namely supervised learning, unsupervised learning and reinforcement learning – and describe the basic ideas of algorithms for solving those problems.
5. Describe the main concepts and principles of major kinds of biologically-inspired algorithms, and understand what is required in order to implement one such technique.
6. Describe how various intelligent-system techniques have been used in the context of several case studies, and compare different techniques in the context of those case studies.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 Discuss and give examples of the role of analogy and metaphor in science and engineering;
2 apply mathematical and computational skills in solving problems;
3 compare different strategies for problem solving, choose a strategy and justify that choice;
4 assess the strengths and weaknesses of hypotheses and techniques;
5 use the library and appropriate internet resources in support of learning.

Notes

  1. Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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