TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Gain a thorough knowledge of the field of Artificial Intelligence 2) Understand the emerging approaches in AI and their implications for information engineering 3) Demonstrate understanding of the applications of AI in business and industry.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	There is no recommended optional programme component for this course.
COURSE DEFINITION	Analytic tools and theories ok micro economics, theories of producer and consumer , determining the relative prices in different markets, existence and stability of price equilibrium; analysis of welfare and general equilibrium.
COURSE CONTENTS	WEEK TOPICS 1st Week Basic concepts, Definition of AI, Overview of application areas 2nd Week Problems and problem solving, State space search, Production rules, Logic, Heuristic search techniques, Generate and test, Hill climbing, search reduction strategies 3rd Week Representation models, Predicate Logic, Rules, Semantic Nets, Frames, Conceptual graphs 4th Week Scripts, Fuzziness and uncertainty 5th Week Fuzzy logic, Statistical techniques for determining probability 6th Week Methodologies for developing knowledge based systems, The KBS development life cycle, knowledge acquisition and elicitation, Management of KBS projects, Prototyping, Implementation, Development environments 7th Week MIDTERM 8th Week Neural networks, Architectures, Hopfield network, Multi-layer perception, Feedforward, Backpropagation, 9th Week Genetic Algorithms, Basic concepts, Population, Chromosomes, Operators 10th Week Schemata, Coding, Rule induction, Basic concepts, Decision tree/rule sets 11th Week Major application areas 12th Week Expert systems, Natural language processing 13th Week Machine vision and robotics 14th Week Data mining and intelligent business support, Internet based application
RECOMENDED OR REQUIRED READING	Peter Jackson, Introduction to Expert Systems, Addison-Wesley (3rd Ed), 1998, ISBN: 0201876868 Goldberg D. E., Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley, 1989, ISBN: 0201157675 Michalski, Bratko, Kubat, Machine Learning and Data Mining, Wiley (3rd Ed), 1999, ISBN: 0471971995 A Bradford, Knowledge Engineering and Management: The CommonKADS Methodology, ISBN-10: 0262193000, ISBN-13: 978-0262193009 Expert Systems: Principles and Programming (Hardcover) Publisher: Course Technology; 4Rev Ed edition (15 Oct 2004), ISBN-10: 0534384471, ISBN-13: 978-0534384470
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Problem Solving,Other
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 10 Project 1 10 Total(%) 50 Contribution of In-term Studies to Overall Grade(%) 50 Contribution of Final Examination to Overall Grade(%) 50 Total(%) 100
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No