BUSINESS ADMINISTRATION
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP4501	Introduction to Artificial Intelligence and Expert Systems	Spring Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi TEVFİK AYTEKİN
Recommended Optional Program Components:	None
Course Objectives:	The course introduces basics of artificial intelligence. Basic search techniques used for problem solving, fundamentals of knowledge representation and logical formalisms, basic learning algorithms, and fundamentals of expert systems will be introduced.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to formulate a state space description of a problem
II. Be able to select and implement brute-force or heuristic algorithm for a problem.
III. Be able to implement minimax search with alpha-beta pruning.
IV. Be able to compare and evaluate the most common models for knowledge representation.
V. Be able to explain the operation of the resolution technique for theorem proving.
VI.Be able to explain the differences among supervised and unsupervised learning.
VII. Be able to explain the concepts of overfitting, underfitting, bias, and variance.
VIII. Be able to implement some of the basic algorithms for supervised learning and unsupervised learning.
IX. Be able to describe fundamentals of expert systems and evaluate them.

Course Content

Introduction to AI, state spaces and searching, heuristic functions and search, alpha-beta pruning, propositional and first-order predicate logic, propositional and first order inference, unification and resolution, linear regression, logistic regression, neural networks and backpropagation algorithm, Bayes’ rule and naive Bayes algorithm, clustering and k-means algorithm, fundementals of expert systems, software for expert systems.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to AI
2)	State spaces and searching.
3)	Heuristic functions and search
4)	Decisions in games, alpha-beta pruning.
5)	Propositional and first-order predicate logic
6)	Propositional and first order inference
7)	Unification and resolution
8)	Linear Regression
9)	Midterm
10)	Logistic Regression
11)	Neural networks and backpropagation algorithm.
12)	Bayes’s rule and naive Bayes algorithm.
13)	Clustering and k-means algorithm
14)	Fundementals of expert systems.
15)	Software for expert systems.

Sources

Course Notes / Textbooks:	Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004.
References:	Yok - None

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 10
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Project	4	20
Homework Assignments	10	20
Quizzes	2	8
Midterms	5	15
Final	5	20
Total Workload		125

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Being able to identify problems and ask right questions
2)	Having problem solving skills and developing necessary analytical attitude
3)	Comprehending theoretical arguments along with counter arguments in detail
4)	Gaining awareness of lifelong learning and being qualified for pursuing graduate education
5)	Applying theoretical concepts in project planning
6)	Communicating efficiently by accepting differences and carrying out compatible teamwork
7)	Increasing efficiency rate in business environment
8)	Developing innovative and creative solutions in face of uncertainty
9)	Researching to gather information for understanding current threats and opportunities in business
10)	Being aware of the effects of globalization on society and business while deciding
11)	Possessing digital competence and utilizing necessary technology
12)	Communicating in at least one foreign language in academic and daily life
13)	Possessing managing skills and competence
14)	Deciding with the awareness of the legal and ethical consequences of business operations
15)	Expressing opinions that are built through critical thinking process in business and academic environment