At the end of this course, the students; 1) Learn the concept of Artificial Intelligence, its subjects and field of interests. 2) Get informed about problem solving techniques, knowledge representation and reasoning. 3) Learn propositional logic and how to program using first order logic. 4) Get informed about how to develop intelligent and self learning systems by understanding machine learning concept. 5) Have background infromation about supervised and unsupervised classification techniques
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Knowledge representation and reasoning. Syntax, semantics, and proof theory (deductive inference) of propositional logic. First-order predicate logic. Uncertainty. Probabilistic reasoning. Expert systems.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to Artificial Intelligence Concept.
2nd Week
Intelligent Agents.
3rd Week
Problem Solving and Searching.
4th Week
Un-informed Searching Strategies.
5th Week
Informed Searching Strategies.
6th Week
Logic Concept, Proposition and inference.
7th Week
Logic Programming: Prolog.
8th Week
Mid-term
9th Week
Problem Solving with Prolog.
10th Week
Machine Learning and General Concepts.
11th Week
Classification Methods.
12th Week
Clustering Algorithms.
13th Week
Introduction to Genetic Algorithm.
14th Week
Introduction to Artificial Neural Networks.
RECOMENDED OR REQUIRED READING
Russell S., Norvig P. Artificial Intelligence: A Modern Approach, Prentice Hall, 2003, Second Edition, ISBN: 9780137903955. Vasif V. Nabiyev, 2010, Yapay Zeka, Seçkin Yayıncılık, 3. Basım, Ankara.
