BM1005 ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION 3 0 0 100
AIM
• By learning various techniques of problem solving, searching and other knowledge representation, artificial intelligence will be formed.
• By understanding different types of pattern recognition techniques and decision making, any patterns in the clinical side can be recognised.
OBJECTIVES
• To study different components of artificial intelligence and basic problem solving
methods.
• To learn the different techniques of pattern recognition and training.
• To learn various rules available in decision making.
• Study the different approaches of pattern classification and application in clinical diagnosis.
UNIT I INTRODUCTION 9
Definition of AI, Intelligent agents, perception and language processing, problem solving, searching, heuristic searching, game playing, Logics, logical reasoning.
UNIT II BASIC PROBLEM SOLVING METHODS 9
Forward Vs Background, knowledge representation, frame problems, heuristic functions, weak methods of matching.
UNIT III PRINCIPLES OF PATTERN RECOGNITION 9
Patterns and features, training and learning in pattern recognition, pattern recognition approach, different types of pattern recognition.
UNIT IV DECISION MAKING 9
Baye’s theorem, multiple features, decision boundaries, estimation of error rates, histogram, kernels, window estimaters, nearest neighbour classification, maximum distance pattern classifier, adaptive decision boundaries.
UNIT V CLUSTER ANALYSIS AND FEATURE EXTRACTION 9
Unsupervised learning, hierarchical clustering, Graph theories approach to pattern clustering, fuzzy pattern classifier, application of pattern recognition in medicine.
TOTAL : 45
TEXT BOOKS
1. Elain Rich and Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw-Hill, 1993.
2. Earl Gose, Richard Johnsonbaugh, Steve Jost, “Pattern Recognition and Inmage Analysis”, Prentice Hall of India Pvt. Ltd., New Delhi, 1999.
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