Computer Engineering Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course General Introduction Information

Course Code:

BYM308

Course Name:

Introduction to Artificial Intelligence

Course Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Course Requirement:

Does the Course Require Work Experience?:

Type of course:

Necessary

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Assoc. Prof. HATİCE ESRA ÖZKAN UÇAR

Course Lecturer(s):

Prof. Dr. Halis ALTUN

Course Assistants:

Course Purpose and Content

Course Objectives:	The aim of this course is to provide students the knowledge about the basic techniques and methodologies of artificial intelligence and abilities to apply artificial intelligence methods on practical problems.
Course Content:	Basic concepts and techniques of AI. Problem solving in AI, informed and uninformed search techniques, Local search techniques and simulated annealing. Meta-heuristic search methods. Introduction to Neural Networks. Game playing, Prolog overview, knowledge representation and reasoning.

Learning Outcomes

The students who have succeeded in this course;
1) Knowledge about the basic methodologies in artificial intelligence.,
2) Ability to use knowledge to formulate, and solve practical problems using artificial intelligence techniques.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introductory terms, foundations, history and philosophy of AI	-
2)	Intelligent Agents	-
3)	Problem Solving and Introduction to Search Methods	-
4)	Uninformed Search Methodologies	-
5)	Heuristic Search	-
6)	Game Playing	-
7)	Meta-Heuristics	-
8)	Neural Networks	-
9)	Knowledge Based Agents	-
10)	First Order Logic	-
11)	Inference in First Order Logic	-
12)	Prolog and Logic Programming	-
13)	Prolog and Logic Programming	-
14)	Probabilistic Reasoning	-

Sources

Course Notes / Textbooks:	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition.
References:	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Medium	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.	3
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.	3
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose	2
4)	Ability to devise, select, and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.	2
5)	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	1
6)	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.	2
7)	Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.	2
8)	Knowledge of the global and societal impacts of engineering practices on priority issues such as health, environment and safety and contemporary issues; knowledge of the legal aspects of engineering solutions. awareness of the consequences	3
9)	Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.	2
10)	Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and knowledge about sustainable development.	3
11)	Ability to design systems to meet desired needs	3
12)	Ability to apply basic sciences in the field of computer engineering	2
13)	Ability to implement designs by experiments	2
14)	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.	2

Learning Activity and Teaching Methods

Alan Çalışması
Anlatım
Beyin fırtınası /Altı şapka
Bireysel çalışma ve ödevi
Course
Grup çalışması ve ödevi
Labs
Homework
Problem Çözme
Uygulama (Modelleme, Tasarım, Maket, Simülasyon, Deney vs.)

Measurement and Evaluation Methods and Criteria

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Sözlü sınav
Homework
Uygulama
Bireysel Proje
Grup Projesi
Sunum

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Application	2	% 30
Midterms	1	% 30
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Aktiviteye Hazırlık	Aktivitede Harçanan Süre	Aktivite Gereksinimi İçin Süre	Workload
Course Hours	10	5			50
Application	3	9			27
Homework Assignments	2	24			48
Midterms	1	24			24
Final	1	24			24
Total Workload					173