| CODE | ICS3206 | ||||||||||||
| TITLE | Machine Learning, Expert Systems and Fuzzy Logic | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | PART ONE – Expert Systems and Fuzzy Logic Expert Systems Expert Systems are considered by many to be the one of the most important contribution of A.I. to the wider world of computing. Hundreds of expert systems have been successfully implemented worldwide. This part of the unit introduces the students to the history, principles, design, and implementation of modern production-rule expert systems. Fuzzy Logic Since it was invented in the 70s to the present day, fuzzy logic has slowly gained in popularity. Fuzzy logic arose from the need for a mathematical formalism to characterize the concept of uncertainty (or fuzziness). Today we find fuzzy logic controllers in automobile transmissions, home appliances, cameras, VCRs, industrial machinery, trains, and many other devices. We introduce the student to the basic principles of fuzzy logic and fuzzy sets and then proceed to study the design and implementation of fuzzy logic controllers as used in many devices. The study-unit assignment will involve the implementation of a Fuzzy Logic Controller. PART TWO – Machine Learning Both pattern recognition and machine learning belong to the most advanced areas of A.I. Numerical methods combined with A.I. techniques have been especially successful in pattern recognition. Research in Machine Learning is now recognised as one of the most important areas of A.I. as well as having application to knowledge acquisition in A.I. systems and contributing to the understanding of human cognition. Topics discussed: - Principles of learning machines, Gold’s Theorem; - Concepts and Categories in Cognitive Science; - Computational learning theory (COLT); - PAC-learning; - Grammatical inference; - Concept learning; - Find-S, Candidate Elimination, and the ID-3 learning algorithms; - Neural Networks. Study-unit Aims: The study-unit aims to: • Introduce students to intermediate-to-advanced machine learning, classification, and pattern recognition problems; • Introduce the concept of deductive learning; • Introduce the concept of fuzzy learning; • Present the theoretical foundations of learning such as PAC learning and VC dimensions; • Enable the student to choose the right algorithms and methods to solve problems; • Elaborate on previous work done in the area; • Prepare students for other study-units, and dissertations that require knowledge of machine learning techniques. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: • Implement and understand basic machine learning algorithms; • Identify problems that can be tackled using these techniques; • Design and implement machine learning-based systems. 2. Skills: By the end of the study-unit the student will be able to: • Use Machine Learning techniques to solve real-world problems; • Choose the right algorithms to solve specific problems; • Combine Machine Learning techniques with other methods (e.g. search and optimization, pattern recognition) to tackle complex problems. Main Text/s and any supplementary readings: - T Mitchell. Machine Learning, McGraw Hill - Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, ISBN 0-201-71159-1 - Study-unit notes and WWW links |
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| RULES/CONDITIONS | Before TAKING THIS UNIT YOU ARE ADVISED TO TAKE CSA1017 OR TAKE ICS1018 OR TAKE ICS2207 OR TAKE ICS2210 | ||||||||||||
| STUDY-UNIT TYPE | Lecture | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Kristian Guillaumier |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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