Area: | Department of Computing |
Credits: | 25.0 |
Contact Hours: | 3.0 |
Lecture: | 1 x 2 Hours Weekly |
Practical: | 1 x 1 Hours Weekly |
Prerequisite(s): | 10163 (v.8) Introduction to Programming Environments 152 or any previous version
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Syllabus: | Artificial Intelligence, its main problems and approaches. Familiarity with symbolic representations, search methods, first-order logics and their applications in reasoning and learning tasks. Familiarity with numerical techniques in AI such as Probabilistic Networks and their applications. |
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Unit Outcomes: | On successful completion of this course students will have gained the ability to apply knowledge of basic science and engineering fundamentals through the application of theories to construct algorithms for intelligent systems. Students will learn how to formulate and solve a variety of problems in the AI domain by adding to their in-depth technical knowledge in problem solving and algorithmic science. |
Texts and references listed below are for your information only and current as of September 30, 2003. Some units taught offshore are modified at selected locations. Please check with the unit coordinator for up-to-date information and approved offshore variations to unit information before finalising study and textbook purchases. |
Unit References: | Winston, O. H., 1993, 'Artificial Intelligence', Addison-Wesley. Genesereth, M. R. and Nilsson, N. J., 1987, 'Logical Foundations of Artificial Intelligence', Morgan Kauffman. Mitchell, T., 1997, 'Machine Learning', McGraw-Hill. |
Unit Texts: | Russel, S. and Norvig P., 1995, ' Artificial Intelligence: A Modern Approach', Prentice-Hall. |
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Unit Assessment Breakdown: | Mid-Semester Test 15%. Assignment 15%. Tutorial Tests 10%. Final Examination 60%. To pass the unit students must gain a minimum of 45% in final examination, 50% in all other assessment sections and an overall assessment score of at least 50%. This is by grade/mark assessment. |
Field of Education: | 31300 Electrical and Electronic Engineering and Technology (Narrow Grouping) | HECS Band (if applicable): | 2 |
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Extent to which this unit or thesis utilises online information: | Informational | Result Type: | Grade/Mark |
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Availability
| Year | Location | Period | Internal | Area External | Central External | | 2004 | Bentley Campus | Semester 1 | Y | | | | 2004 | Sri Lanka Inst Info Tech | Semester 2 | Y | | |
Area
External | refers to external course/units run by the School or Department, offered online or through Web CT, or offered by research. |
Central
External | refers to external course/units run through the Curtin Bentley-based Distance Education Area |
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