At the end of this course, the students; 1) Be informed of Knowledge representation and reasoning. 2) Know syntax, semantics, and proof theory (deductive inference) of propositional logic. 3) Apply first-order predicate logic. 4) Have knowledge of uncertainty and probabilistic reasoning. 5) Recognize expert systems.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
COURSE DEFINITION
Semantic nets and description matching. Generate and test, Means-ends analysis, and problem reduction. Nets and basic search. Nets and optimal search. Trees and adversarial search. Rules and rule chaining. Rules, substrates, and cognitive modeling. Frames and inheritance. Frames and commonsense. Numeric constraints and propagation. Symbolic constraints and propagation. Logic and resolution proof. Backtracking and truth maintenance. Planning. Learning by analyzing differences. Learning by explaining experience. Learning by correcting mistakes. Learning by recording cases. Learning by managing multiple models. Learning by building identification trees. Learning by training neural nets. Learning by training perceptrons. Learning by training approximation nets. Learning by simulating evolution. Recognizing objects. Describing images. Expressing language constraints. Responding to questions and commands.
COURSE CONTENTS
Knowledge representation and reasoning. Nets, basic and optimal search. Trees and adversarial search. Rules and rule chaining. Neural networks.
RECOMENDED OR REQUIRED READING
1) Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods, 3rd edition 2) M. Sonka, V. Hlavac, R. Boyle, "Image Processing, Analysis, and Machine Vision". 3) A. K. Jain, "Fundamentals of Digital Image Processing", Prentice-Hall.
