COSC367-14S2 (C) Semester Two 2014

Computational Intelligence

15 points

Details:
Start Date: Monday, 14 July 2014
End Date: Sunday, 16 November 2014
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 25 July 2014
  • Without academic penalty (including no fee refund): Friday, 10 October 2014

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.

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

Prerequisites

Restrictions

COSC329

Timetable Note

Depending on final student numbers, some of the advertised lab/tutorial streams may not run. Final lab/tutorial options will be available for self-allocation closer to the start of the semester through My Timetable.

Course Coordinator / Lecturer

Kourosh Neshatian

Lecturer

Richard Green

Assessment

Assessment Due Date Percentage 
Lab Quizzes 20%
Assignment 20%
Term Test 10%
Exam 50%

Textbooks / Resources

Recommended Reading

Poole, David L. , Mackworth, Alan K; Artificial intelligence : foundations of computational agents ; Cambridge University Press, 2010.

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

Notes

There are several important documents available online about departmental regulations, policies and guidelines at the following site. We expect all students to be familiar with these.

Notices about this class will be posted to the class forum in the Learn system.

COSC students will also be made members of a class called “CSSE Notices”, where general notices will be posted that apply to all classes (such as information about building access or job opportunities).

Additional Course Outline Information

Grade moderation

The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:
1. You must achieve an average grade of at least 50% over all assessment items.
2. You must achieve an average mark of at least 45% on invigilated assessment items.
If you satisfy both these criteria, your grade will be determined by the following University- wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a B- grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.

Aegrotats
If factors beyond your control (such as illness or family bereavement) prevent you from completing some item of course work (including laboratory sessions), or prevent you from giving your best, then you may be eligible for aegrotat, impaired performance consideration or an extension on the assessment. Details of these may be found in the University Calendar. Supporting evidence, such as a medical certificate, is normally required. If in doubt, talk to your lecturer.

Indicative Fees

Domestic fee $748.00

International fee $3,388.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

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

  • COSC367-14S2 (C) Semester Two 2014
Previous year Next year