CHILD DEVELOPMENT (TURKISH)
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	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 :	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 gain both theoretical and practical knowledge about physical, cognitive, social-emotional aspects of child development.	4
2)	To display actions in professional practice based on ethical principles and values.	5
3)	To adopt the principle of lifelong learning, using efficient ways for accessing information.	5
4)	To know the stages of child development and to be able to use models / theories efficiently for supporting children's cognitive, affective and psycho-motor development.	5
5)	To plan, implement and evaluate professional projects, research and events with a sense of social responsibility,	5
6)	To be able to use effective communication methods in counseling and child and family-based guidance.	3
7)	To be sensitive to the child and family-related issues taking into account the child's stages of development, and to implement strategies for personal development of child and education methods which are vital for leading effective and productive life.	5
8)	To use the education and communication materials according to the child development stage, and to create proper educational environment.	5
9)	To take responsibilities in the field of child development and education using interdisciplinary approach, and to use information technologies, and to engage in projects and activities.	5
10)	To use health information technologies for research in the field of child development.	5
11)	To be able to monitor occupational information using at least one foreign language, to collaborate and communicate with colleagues at international level.	5
12)	To become a good example for colleagues and society, and represent efficiently the professional identity using advanced knowledge about child development.	5