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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	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 :	Assist. Prof. BARIŞ ÖZCAN
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)	Constraint Satisfaction Problems
4)	Searching with other agents.
5)	Markov decision processes I
6)	Markov decision processes II
7)	Midterm
8)	Reinforcement Learning
9)	Probability, Bayes' Rule and Bayes Nets
11)	Bayes’s rule and naive Bayes algorithm.
12)	Neural networks and backpropagation algorithm I
13)	Neural Networks and backpropagation algorithm II
14)	Large Language Models I
15)	Large Language Models II

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	5	% 20
Project	1	% 25
Midterms	1	% 20
Final	1	% 35
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	7	35
Homework Assignments	10	20
Quizzes	6	16
Midterms	5	15
Final	5	20
Total Workload		148

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 students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	2
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising
4)	To be able to follow and interpret innovations in the field of advertising
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.
6)	To address and solve the needs and problems of the field through the developed scientific perspective
7)	To recognize and understand all the dynamics within the field of advertising
8)	To analyze and develop solutions to problems encountered in the practical field of advertising