CENGT004 Artificial Intelligence

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan

Information

Code CENGT004
Name Artificial Intelligence
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal

The design of computers by modeling the learning mechanism of the human brain is gaining importance day by day. The content of the course generally includes intelligent search, intelligent game playing approaches, intuitive problem solving approaches, artificial networks. The main purpose of the course is to provide the necessary infrastructure to solve new artificial intelligence problems that may be encountered in life.

Course Content

Introduction to artificial intelligence, heuristic problem solving approach, artificial intelligence in games, learning methods, artificial neural networks, convolutional neural networks, iterative neural networks, deep belief networks, natural language processing, expert systems, artificial intelligence optimization algorithms.

Course Precondition

none

Resources

T. Mitchell, "Machine Learning", McGraw-Hill, 1997. C. M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2007. S. Haykin, "Neural Networks and Learning Machines", Prentice Hall, 2008. R. O. Duda, Pattern Classification, Wiley-Interscience, 2000.

Notes

papers


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Understanding the basics of artificial intelligence
LO02 To learn the basics of developing programs and machines similar to human and animal thinking systems,
LO03 Learning and using the methods and algorithms used in artificial intelligence,
LO04 Acquiring the skills to produce solutions with artificial intelligence methods suitable for the problems encountered


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Belirsiz


Week Plan

Week Topic Preparation Methods
1 Introduction to artificial intelligence Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Intuitive problem solving approach Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Artificial intelligence in games Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Learning methods Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Artificial networks - artificial neural networks Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Artificial networks - convolutional neural networks Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Artificial networks - recursive neural networks, deep belief networks Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Reading Lecture Notes and Resources Ölçme Yöntemleri:
Yazılı Sınav
9 Natural language processing Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
10 Natural language processing Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
11 Expert systems Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Artificial intelligence optimization algorithms Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
13 Artificial intelligence optimization algorithms Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
14 Student applications and article studies Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
15 Student applications and article studies Reading Lecture Notes and Resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap
16 Term Exams Reading Lecture Notes and Resources Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Reading Lecture Notes and Resources Ölçme Yöntemleri:
Yazılı Sınav