|Course title||Advanced Topics in Artificial Intelligence
|Course description|| The course introduces fuzzy logic and applications. Fuzzy expert systems. Fuzzy query. Fuzzy data and knowledge engineering. Fuzzy control. Genetic algorithms and programming and their applications. Parallel genetic algorithms. Island model and coevolution. Genetic programming. Introduction to emergent computing.
|Exclusion||CMSC5707 or CSCI6200|
|Semester||1 or 2|
|Grade Descriptors||A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
|Learning outcomes||At the end of the course of studies, students will have acquired the ability to
1. do knowledge and data engineering in real life applications;
2. build fuzzy expert systems in specific domains;
3. develop applications using fuzzy queries; and
4. carry out research using evolutionary algorithms.
(for reference only)
|Recommended Reading List||1. Leung K.S. and (Lam W.), “Fuzzy Concepts in Expert Systems” – IEEE COMPUTER, Vol.21, No.9, pp.43-56, Sept., 1988.
2. M.L. Wong and K.S. Leung, Data Mining Using Grammar Based Genetic Programming and Applications, Jan 2000, Kluwer Academic Publishers, Genetic Programming Series.
3. (Wong M.H). and Leung K. S., “A Fuzzy Database-Query Language” – Information Systems, Pergamon Press, Vol.15, No.5, pp.583-590, Oct.,1990.
4. K.S. Leung and (M.H. Wong), “An Expert System Shell Using Structured Knowledge : An Object Oriented Approach”, IEEE COMPUTER, Vol.23, No.3, pp. 38-47, March 1990.
5. Leung K.S., (Wong M.H.) and (Lam W.), “A Fuzzy Expert Database System” – Data & Knowledge Engineering, North-Holland, Vol.4, No.4, pp.287-304, Dec., 1989.
|CSCIN programme learning outcomes||Course mapping|
|Upon completion of their studies, students will be able to:|
|1. identify, formulate, and solve computer science problems (K/S);|
|2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
|3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);|
|4. communicate effectively (S/V);
|5. succeed in research or industry related to computer science (K/S/V);
|6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);|
|7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);|
|8. practise high standard of professional ethics (V);|
|9. draw on and integrate knowledge from many related areas (K/S/V);
|Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured|