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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)	To be able to apply theoretical concepts related to mass communication, consumer behavior, psychology, persuasion,sociology, marketing, and other related fields to understand how advertising and brand communication works in a free-market economy.	2
2)	To be able to critically discuss and interpret theories, concepts, methods, tools and ideas in the field of advertising.	2
3)	To be able to research, create, design, write, and present an advertising campaign and brand strategies of their own creation and compete for an account as they would at an advertising agency.	2
4)	To be able to analyze primary and secondary research data for a variety of products and services.	2
5)	To be able to develop an understanding of the history of advertising as it relates to the emergence of mass media outlets and the importance of advertising in the marketplace.	2
6)	To be able to follow developments, techniques, methods, as well as research in advertising field; and to be able to communicate with international colleagues in a foreign language. (“European Language Portfolio Global Scale”, Level B1)	2
7)	To be able to take responsibility in an individual capacity or as a team in generating solutions to unexpected problems that arise during implementation process in the Advertising field.	3
8)	To be able to understand how advertising works in a global economy, taking into account cultural, societal, political, and economic differences that exist across countries and cultures.	2
9)	To be able to approach the dynamics of the field with an integrated perspective, with creative and critical thinking, develop original and creative strategies.	2
10)	To be able to to create strategic advertisements for print, broadcast, online and other media, as well as how to integrate a campaign idea across several media categories in a culturally diverse marketplace.	2
11)	To be able to use computer software required by the discipline and to possess advanced-level computing and IT skills. (“European Computer Driving Licence”, Advanced Level)	2
12)	To be able to identify and meet the demands of learning requirements.	2
13)	To be able to develop an understanding and appreciation of the core ethical principles of the advertising profession.	2