4517 (v.6) Artificial and Machine Intelligence 251


Area:Department of Computing
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
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.
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.
Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.
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.
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.
YearLocationPeriodInternalArea ExternalCentral External
2004Bentley CampusSemester 1Y  
2004Sri Lanka Inst Info TechSemester 2Y  


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