M.SC. in Computer Engineering (Without Thesis)

CE 533 | Course Introduction and Application Information

Course Name

Artificial Intelligence

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 533	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Problem Solving Q&A Critical feedback Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Dr. Öğr. Üyesi Alper Demir
Assistant(s)	-

Course Objectives

Artificial Intelligence (AI) is devoted to the computational study of intelligent behavior. The element that the fields of AI have in common is the creation of agents/machines that can "think". This course will cover a broad technical introduction to the techniques that enable agents/computers to behave intelligently: problem solving, representing knowledge, reasoning, learning, perceiving, and interpreting. The bulk of this course reflects this diversity. We will examine the fundamental questions and issues of AI and will explore the essential techniques. In the special topics, several AI applications will be presented.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to discuss a broad range of issues in the field of AI.
2	will be able to use and discuss the basic techniques of the field.
3	will be able to evaluate potential applications of AI technology.
4	will be able to identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
5	will be able to implement basic AI algorithms (e.g., standard search algorithms).

Course Description

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction	Chapter 1
2	Intelligent Agents	Chapter 2
3	Solving Problems by Searching	Chapter 3
4	Local Search	Chapter 4
5	Adversarial Search	Chapter 5
6	Constraint Satisfaction Problems	Chapter 6
7	Logical Agents, Propositional Logic	Chapter 7
8	First-Order Logic	Chapter 8
9	Inference in FOL	Chapter 9
10	Classical Planning	Chapter 10
11	Uncertainty	Chapter 13 & 14
12	Learning	Chapter 18
13	Reinforcement Learning	Chapter 21
14	Reinforcement Learning	Chapter 21
15	Paper Presentations
16	Final Review

Course Notes/Textbooks	S.Russell, P.Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2010
Suggested Readings/Materials	Nick Bostrom, Superintelligence: Paths, Dangers, Strategies

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	3	15
Presentation / Jury	1	25
Project
Seminar / Workshop
Oral Exams
Midterm	1	20
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	5	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	15	4	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	3	10	30
Presentation / Jury	1	12	12
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	35	35
Final Exam	1	40	40
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	X	-	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	X	-	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	X	-	-	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		X	-	-	-	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	X	-	-	-
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	X	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	X	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	X	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	X	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		X	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		X	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-