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COSC367-12S2 (C) Semester Two 2012
Computational Intelligence

15 points, 0.1250 EFTS
09 Jul 2012 - 11 Nov 2012
↓Other occurrences

Description

This course introduces Computational Intelligence computing concepts and demonstrates how they are used to solve problems that are normally difficult or intractable by conventional means. Topics covered include artificial intelligence programming languages, logic representations, biologically inspired algorithms and computer vision. Practical work will focus on solving sample problems using these various techniques.

This course introduces Artificial Intelligence (AI) techniques and demonstrates how they are used to solve problems that are normally difficult or intractable by conventional means.

The course covers core concepts in AI including problem solving by search, knowledge representation and reasoning, symbolic AI languages, decision making, biologically-inspired algorithms, learning and computer vision. Practical work will focus on solving sample problems using these various techniques.

Learning Outcomes

In this course students will learn about artificial intelligence techniques and their applicability to real problems. They will design and implement artificial intelligence-based solutions to non-trivial
problems. Having completed the course students will be able to do the following:

*   Know the conceptual model behind AI languages (Lisp and Prolog) and apply this knowledge to create efficient programs.
*   Design and implement an expert system that operates in a realistic problem domain
*   Represent concepts and logic computationally in several different ways and compare and contrast the capabilities and limitations these representations
*   Design solutions for problems involving uncertain inputs or outcomes
*   Apply heuristic search to optimisation problems
*   Describe techniques for computer vision and compare their strengths and weaknesses
*   Describe how artificial intelligence is used in advanced education software and what benefits this brings to the student (or teacher)
*   Apply machine learning algorithms to real data sets to build predictive models
*   Knowledge of current and emerging AI technologies
*   The ability to design and implement computing solutions using artificial intelligence techniques
*   The ability to objectively compare AI techniques and use the right ones to develop an appropriate solution

Pre-requisites

Restrictions

COSC329, COSC230
Lectures
Streams Day Time Where Notes
Stream 01 Thursday 9:00am-10:00am E11 Lecture Theatre 9 Jul - 19 Aug,
3 Sep - 14 Oct
Friday 10:00am-11:00am E6 Lecture Theatre 9 Jul - 19 Aug,
3 Sep - 14 Oct

Labs
Streams Day Time Where Notes
Stream 01 Friday 1:00pm-3:00pm Erskine 131 Lab 1 (Computer Lab) 9 Jul - 19 Aug,
3 Sep - 14 Oct
Stream 02 Wednesday 3:00pm-5:00pm Erskine 131 Lab 1 (Computer Lab) 9 Jul - 19 Aug,
3 Sep - 14 Oct

Course Coordinator / Lecturer

Kourosh Neshatian

Lecturer

Richard Green

Assessment

Assessment Due Date Percentage 
Assignment 1 17 Aug 2012 20%
Assignment 2 12 Oct 2012 20%
Final Exam 60%

Examination and Formal Tests

Exam Friday 02 Nov 2012 9:30am-12:30pm  

Textbooks

Recommended Reading

Stuart Russell, Peter Norvig; Artificial Intelligence: A Modern Approach; 3rd; Prentice Hall, 2009.

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Additional Course Outline Information

Notes

Course Outline is available here: http://www.cosc.canterbury.ac.nz/open/teaching/classes/cosc367/outline.pdf

Fees

Domestic fee $692.00
International fee $3,200.00

Minimum enrolments

This course will not be offered if fewer than 10 people apply to enrol.


For further information see Computer Science and Software Engineering.

All COSC367 Occurrences

  • COSC367-12S2 (C) Semester Two 2012
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