ADVANCED ARTIFICIAL INTELLIGENCE PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
MKF531	ADVANCED ARTIFICIAL INTELLIGENCE	1	3	3	6

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	MASTER'S DEGREE, TYY: + 7.Level, EQF-LLL: 7.Level, QF-EHEA: Second Cycle
Type of the Course :	Elective
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Prof. HAMDİ ALPER ÖZYİĞİT
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course aims to introduce basic concepts and different approaches to Artificial Intelligence (AI) (including symbolic and non-symbolic ones). It also aims at extending the computer engineering vision of the student, and evaluating the possible research potentials of the students on the subject.
Contents of the Course Unit:	Intelligent agents. Problem solving by searching. Informed/uninformed search methods. Exploration. Constraint satisfaction problems. Knowledge and reasoning: first-order logic, knowledge representation. Learning. Selected topics: neural networks, natural computing.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
The ability to design an agent for a given problem.
The ability to understand the problems and principles of searching for solution; to distinguish among variety of search algorithms.
The ability to comprehend first order logic and inference procedure in finding solutions to logical problems.
The ability to describe the fundamentals for machine learning.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Related chapter in sources	Intelligent Agents. Problem Solving by Searching,	-
2	Related chapter in sources	Informed/Uninformed Search Methods, Exploration	-
3	Related chapter in sources	Local search, search with non deterministic actions and partial observation	-
4	Related chapter in sources	Adversarial Search and constraint satisfaction	-
5	Related chapter in sources	Logical Agents and first order logic	-
6	Related chapter in sources	Inference in first order logic	-
7	Related chapter in sources	Planning and acting in real world	-
8	-	MID-TERM EXAM	-
9	Related chapter in sources	Knowledge representation	-
10	Related chapter in sources	Uncertain Knowledge and Reasoning. Probabilistic reasoning	-
11	Related chapter in sources	Making simple and complex Decisions	-
12	Related chapter in sources	Learning from examples. Knowledge in learning	-
13	Related chapter in sources	Learning probabilistic models. Reinforcement learning	-
14	Related chapter in sources	Selected Topics	-
15	Related chapter in sources	Review the notes	-
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Artificial Intelligence : A Modern Approach (Second Edition), Stuart Russell and Peter Norvig, Prentice-Hall, 2003, ISBN: 0-13-790395
Ant Colony Optimization, Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN: 0-262-04219-3.
Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Based on the engineering degree level qualifications, Mechatronics Engineering or a different field of information can improve the level of expertise.

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Mechatronics Engineering can grasp interdisciplinary interaction to be associated with.

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The knowledge gained in the field of Mechatronics Engineering integrating the information gathered from different disciplines can interpret and create new knowledge.
2	You can use the theoretical and practical knowledge acquired in the level of expertise in Mechatronics Engineering.

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Problems related to the field of Mechatronics Engineering may be using research methods.

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1
2
3

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1
2
3
4

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1
2
3

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	0	0	0
Preliminary & Further Study	0	0	0
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	0	0	0
Preparation for the Final Exam	0	0	0
Mid-Term Exam	0	0	0
Preparation for the Mid-Term Exam	0	0	0
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	0	0	0