PUBLIC RELATIONS AND PUBLICITY
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 prepare the students to become communication professionals by focusing on strategic thinking, professional writing, ethical practice and innovative use of traditional and new media
2)	To be able to create effective public relations plans using fundamental planning components that include situation analysis, public profile, objectives, strategies and tactics.
3)	To be able to apply theoretical concepts related to mass communication, consumer behavior, psychology, persuasion,sociology, marketing, and other related fields to understand how public realtions works.
4)	To be able to have the ability to explain and identify problems associated with the relationships between events and facts in the areas of public relations, persuasive communication, communication management, corporate communications.
5)	To be able to analyze primary and secondary research data in the fields of perception and reputation management and corporate communication practices.
6)	To be able to search, write, and design articles, newsletters, and fliers, brochures, and announcements, in styles and formats appropraite various audiences, mediums and settings.
7)	To be able to apply the underlying theories of communication and the necessities of work safety to different types of public relations processes and campaigns.
8)	To be able to develop creative and persuasive management skills in terms of reputation, employee relations, leadership and similar corporate practices.
9)	To be able to take responsibility in an individual capacity or as a team in generating solutions to given scenarios which can occur in public relations processes.
10)	To be able to understand how an organizational culture works and how employees and leaders create messages as a communication tool.
11)	To be able to critically discuss and interpret theories, concepts, methods, tools and ideas in the field of public relations.
12)	To be able to to use information, communication technologies and computer software with the required level of public relations, marketing communication, persuasive communication, communication management, corporate communications.
13)	To be able to explain and describe business marketing activities, economics, business law and global business practices.
14)	To be able to recognize national and international, social and cultural dimensions of public relations.