Introduction to Intelligent Systems - CO528

Location Term Level Credits (ECTS) Current Convenor 2018-19
Canterbury Spring
View Timetable
5 15 (7.5) DR D Chu


CO520 Further Object-Oriented programming





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.


This module appears in:

Contact hours

22 lectures

Indicative reading

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

Explain the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature of intelligence and learning.
Describe the main kinds of state-space search algorithms, discussing their strengths and limitations
Explain the main concepts and principles associated with different kinds of knowledge representation, such as logic, case-based representations, and sub-symbolic/connectionist representations
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
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
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

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