Page 42 - REC :: M.E. CSE Curriculum and Syllabus - R2019

CP19P21 INTELLIGENT SYSTEMS Category L T P C
PE 3 0 0 3

Objectives:
Upon completion of this course, students will demonstrate good knowledge of basic theoretical foundations of the
⚫
various common intelligent systems methodologies.
⚫ Students will be able to deal with uncertainty
⚫ Will understand the concepts of Genetic algorithm and fuzzy inference
⚫ Will have clear understanding on the Neural Network concepts
⚫ Students will determine which type of intelligent system methodology would be suitable for a given

UNIT-I INTRODUCTION TO INTELLIGENT SYSTEMS 9
Introduction to knowledge-based intelligent systems - Intelligent machines, or what machines can do? , The history of
artificial intelligence, or from the ‘Dark Ages’ to knowledge-based systems. Rule-based expert systems - Rules as a
knowledge representation technique, The main players in the expert system development team, Structure of a rule-
based expert system, Fundamental characteristics of an expert system, Forward chaining and backward chaining
inference techniques .MEDIA ADVISOR: a demonstration rule-based expert system, Conflict resolution, Advantages
and disadvantages of rule-based expert

UNIT-II UNCERTAINITY LOGIC 9
What is uncertainty? , Basic probability theory, Bayesian reasoning, Bias of the Bayesian method, Certainty factors
theory and evidential reasoning, Comparison of Bayesian reasoning and certainty factors, Introduction to Fuzzy Logic,
Fuzzy sets ,Linguistic variables and hedges, Operations of fuzzy sets, Fuzzy rules, Fuzzy inference, Building a fuzzy
expert system.

UNIT-III NEURAL NETWORKS 9
How the Brain Works? Neural Networks, Simple computing elements, Network structures Optimal network structure,
Perceptron’s, What perceptrons can represent? Multilayer Feed-Forward Networks, Back-propagation learning,Back-
propagation as gradient descent search Applications of Neural Networks, Bayesian Methods for Learning Belief
Networks Bayesian learning, Belief network learning problems, A comparison of belief networks and neural networks,
Boltzman training, Combined back propagation ,Cauchy training.

UNIT-IV HYBRID INTELLIGENT SYSTEMS 9
Genetic algorithms -Why genetic algorithms work?, Genetic programming, Introduction or how to combine German
mechanics with Italian love, Neural expert systems, Neuro-fuzzy systems, ANFIS: Adaptive Neuro-Fuzzy Inference
System, Evolutionary neural networks ,Fuzzy evolutionary systems.

UNIT-V LEARNING &KNOWLEDGE ENGINEERING 9
Learning: Learning from Observation, General Model of Learning Agents, Inductive Learning, Learning Decision
Trees, Rote Learning, Learning by Advice, Learning in Problem Solving, Explanation based Learning. Knowledge
engineering : Introduction, an expert system for a particular problem, a fuzzy expert system working for a problem, a
neural network working for a problem.

Total Contact Hours : 45

Course Outcomes:
Upon completion of the course, students will be able to
⚫ Students will gain deep understanding of the basic artificial intelligence techniques.
⚫ Students will gain the knowledge to deal with uncertainty.
⚫ Students will apply their knowledge to design solutions to different problems.
⚫ Students will go for various learning techniques and also can apply them.
⚫ Students will have the ability to design and develop an intelligent system for a selected application.