EDUCATIONAL TECHNOLOGY (ENGLISH, THESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5103	Artificial Intelligence	Spring	3	0	3	8

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:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi TEVFİK AYTEKİN
Course Lecturer(s):	Prof. Dr. NAFİZ ARICA
Recommended Optional Program Components:	None
Course Objectives:	The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to solve problems by applying a suitable search method.
II. Be able to implement minimax search and alpha-beta pruning in game playing.
III. Be able to use logical formalisms in modeling.
IV. Be able to apply supervised learning techniques to a given problem.
V. Be able to apply unsupervised learning techniques to a given problem.
VI. Be able to use the basic techniques in natural language processing.

Course Content

introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Uninformed Search Strategies
3)	Uninformed Search Strategies
4)	Informed (Heuristic) Search Strategies
5)	Informed (Heuristic) Search Strategies
6)	Adversarial Search
7)	Propositional Logic
8)	Predicate logic
9)	Supervised Learning Techniques
10)	Supervised Learning Teknileri
11)	Unsupervised Learning Techniques
12)	Unsupervised Learning Techniques
13)	Natural Language Processing
14)	Natural Language Processing
15)	Final Exam

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:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 10
Project	1	% 50
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 10
PERCENTAGE OF FINAL WORK		% 90
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	3	42
Project	1	42	42
Homework Assignments	7	8	56
Final	1	12	12
Total Workload			194

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 develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2)	To be able to comprehend the interdisciplinary interaction with which the field is related.
3)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
4)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
5)	To be able to solve the problems encountered in the field by using research methods.
6)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
7)	To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary.
8)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
9)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
10)	To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes.
11)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
12)	Being able to independently carry out a work that requires expertise in the field.
13)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
14)	Being able to lead in environments that require the resolution of problems related to the field.