Advanced Artificial Intelligence (MDES677) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Advanced Artificial Intelligence	MDES677	Elective Courses	3	0	0	3	5

Pre-requisite Course(s)
Consent of the Instructor

Course Language	English
Course Type	Elective Courses Taken From Other Departments
Course Level	Ph.D.
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	To introduce advanced concepts and different approaches to Artificial Intelligence (AI) (including symbolic and non-symbolic ones). To extent the engineering vision of the student.
Course Learning Outcomes	The students who succeeded in this course; To learn how to design an agent for a given problem. To be able to decide on and apply suitable AI technique(s) to a given problem
Course Content	Intelligent agents, problem solving by searching, informed/uninformed search methods, exploration, constraint satisfaction problems, game playing, knowledge and reasoning: first-order logic, knowledge representation, learning, selected topics: evolutionary computing, multiagent systems, artificial neural networks, ant colony optimization.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Intelligent Agents	Chapters 1-2 from Russell & Norvig
2	Intelligent Agents	Chapter 1-2 from Russell & Norvig
3	Informed/Uninformed Search Methods, Exploration	Chapter 3-4 from Russell & Norvig
4	Informed/Uninformed Search Methods, Exploration	Chapter 3-4 from Russell & Norvig
5	Constraint Satisfaction Problems	Chapter 5 from Russell & Norvig
6	Constraint Satisfaction Problems	Chapter 5 from Russell & Norvig
7	Game Playing	Chapter 6 from Russell & Norvig
8	Knowledge and Reasoning : Logical Agents	Chapter 7 from Russell & Norvig
9	Knowledge and Reasoning : First-Order Logic	Chapter 8 from Russell & Norvig
10	Knowledge and Reasoning : Inference in First-Order Logic	Chapter 9 from Russell & Norvig
11	Selected Topics : Evolutionary Computing	Source #5
12	Selected Topics : Multiagent Systems	Source #4
13	Selected Topics : Neural Networks	Source #3
14	Selected Topics : At Colony Optimization	Source #1
15	Overall review	-
16	Final exam	-

Sources

Course Book	1. Artificial Intelligence: A Modern Approach (Second Edition). Stuart Russell and Peter Norvig, Prentice-Hall, 2003, ISBN: 0-13-790395
Other Sources	2. Ant Colony Optimization, Marco Dorigo and Thomas Stützle, MIT Press, 2004.
	3. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992.
	4. Introduction to the Theory of Neural Computation, J. Hertz, A. Krogh and R.G. Palmer, Addison-Wesley Publishing Company, 1991
	5. An Introduction to MultiAgent Systems, Wooldridge, M., John Wiley & Sons, 2002
	6. An Introduction to Genetic Algorithms, Melanie Mitchell, MIT Press, 1998

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	-	-
Presentation	1	10
Project	1	25
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	25
Final Exam/Final Jury	1	40
Toplam	4	100

Percentage of Semester Work	60
Percentage of Final Work	40
Total	100

Course Category

Core Courses
Major Area Courses	X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains the ability to understand and apply knowledge in the fields of mathematics, science and basic sciences at the level of expertise.
2	Gains the ability to access wide and deep knowledge in the field of Engineering by doing scientific research with current techniques and methods, evaluate, interpret and implement the gained knowledge.
3	Being aware of the latest developments his/her field of study, defines problems, formulates and develops new and/or original ideas and methods in solutions.
4	Designs and applies theoretical, experimental, and model-based research, analyzes and interprets the results obtained at the level of expertise.
5	Gains the ability to use the applications, techniques, modern tools and equipment in his/her field of study at the level of expertise.
6	Designs, executes and finalizes an original work process independently.
7	Can work in interdisciplinary and interdisciplinary teams, lead teams, use the information of different disciplines together and develop solution approaches.
8	Pays regard to scientific, social and ethical values in all professional activities and acquires responsibility consciousness at the level of expertise.
9	Contributes to the literature by communicating the processes and results of his/her academic studies in written form or orally in national and international academic environments, communicates effectively with communities and scientific staff working in the field of specialization.
10	Gains the skill of lifelong learning at the level of expertise.
11	Communicates verbally and in written form using a foreign language at least at the European Language Portfolio B2 General Level.
12	Recognizes the social, environmental, health, safety, legal aspects of engineering applications, as well as project management and business life practices, being aware of the limitations they place on engineering applications.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration	3	5	15
Project	1	20	20
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	8	8
Prepration of Final Exams/Final Jury	1	10	10
Total Workload			133