BPP239 Artificial intelligence

3 ECTS - 2-0 Duration (T+A)- 3. Semester- 2 National Credit

Information

Code BPP239
Name Artificial intelligence
Term 2022-2023 Academic Year
Semester 3. Semester
Duration (T+A) 2-0 (T-A) (17 Week)
ECTS 3 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Ön Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Öğr. Gör.Dr. YILMAZ KOÇAK
Course Instructor Öğr. Gör.Dr. YILMAZ KOÇAK (A Group) (Ins. in Charge)


Course Goal / Objective

To create a general culture on artificial intelligence concepts and techniques, to gain the knowledge and skills to analyze and write programs at the basic level with artificial neural networks, deep networks and other intelligent techniques.

Course Content

Definition of artificial intelligence, general structure of algorithms and systems, expert systems, artificial neural networks, deep networks and deep learning, optimization algorithms, logic and fuzzy logic, programming languages for artificial intelligence.

Course Precondition

No

Resources

Elmas, Ç, Applications of Artificial Intelligence, Seçkin Publisher, 2016, Ankara Karaboğa, D, Optimization Algorithms of Artificial Intelligence, Nobel Publisher, 2010, Kayseri Yılmaz, A, Artificial Intelligence, KODLAB, 2017, İstanbul Manaswi NK, Deep Learning with Applications Using Python, Apress, 2018 India Wirsansky E, Hands On Genetics Algorithms with Python, Packt, 2020, Birmingham Özgül, F, Python in All Aspects, Kodlab, 2020

Notes

Artificial intelligence, Lecture Notes, Dr. Yılmaz KOÇAK


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Explains artificial intelligence concept and algorithms.
LO02 Explains articial neural networks and deep networks.
LO03 Expresses fuzzy sets and fuzzy logic.
LO04 Expresses artificial intelligence optimization algorithms such as genetic algorihtms, colony concepts.
LO05 Prepares basics program using programing languages used artificial intelligence (Python, Matlab etc.).
LO06 Applies artificial intelligence algorithms using a selected programming language.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Yetkinlikler - İletişim ve Sosyal Yetkinlik Communicates effectively with all partners on a sectoral basis.
PLO02 Bilgi - Kuramsal, Olgusal has the basic knowledge necessary to develop computer software, to establish algorithm, sequential and simultaneous flow logic 3
PLO03 Yetkinlikler - Alana Özgü Yetkinlik Designs systems for fundamental problems in microcontrollers, embedded systems and analog/digital electronics.
PLO04 Yetkinlikler - Alana Özgü Yetkinlik Uses basic software related to information and communication technologies, specific to his profession. 2
PLO05 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Applies the software and hardware developments in the field of Computer Programming independently. 3
PLO06 Bilgi - Kuramsal, Olgusal Explains the necessary methods for solving well-defined problems in the field of Computer Technologies and Programming. 4
PLO07 Bilgi - Kuramsal, Olgusal Has the basic knowledge level required to develop software specific to web, mobile and other electronic platforms. 3
PLO08 Beceriler - Bilişsel, Uygulamalı Develops software for desktop and other environments. 3
PLO09 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Takes an active role in project development processes, independently or as part of a group, within a planned project.
PLO10 Bilgi - Kuramsal, Olgusal Knows project planning, development and implementation processes.
PLO11 Yetkinlikler - Alana Özgü Yetkinlik Performs data storage, editing, querying, etc. operations in computer and network environment.
PLO12 Yetkinlikler - Alana Özgü Yetkinlik It has the ability to solve unpredictable hardware and software problems.
PLO13 Beceriler - Bilişsel, Uygulamalı Codes software components that have been analyzed and the algorithm has been prepared. 4
PLO14 Bilgi - Kuramsal, Olgusal Knows the methods to be used in software development. 4
PLO15 Yetkinlikler - Öğrenme Yetkinliği Constantly follows current innovations and developments in the field of information technologies. 2
PLO16 Yetkinlikler - İletişim ve Sosyal Yetkinlik Communicates verbally and in writing in a foreign language.
PLO17 Yetkinlikler - İletişim ve Sosyal Yetkinlik It has the phenomenon of the necessity of moral and ethical behaviors related to the information technology profession.
PLO18 Yetkinlikler - Alana Özgü Yetkinlik Has the necessary awareness of occupational safety in her field.
PLO19 Beceriler - Bilişsel, Uygulamalı It uses operating systems with administrative features.
PLO20 Bilgi - Kuramsal, Olgusal Has basic knowledge of entrepreneurship.


Week Plan

Week Topic Preparation Methods
1 Introduction to Artificial Intelligence Review of Source Book Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Artificial Neural Networks and Basic Elements Learning about artificial neural networks Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Creation of Artificial Neural Networks Learning about artificial neural networks Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
4 Structures of Artificial Neural Networks Research about structures of artificial neural networks Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
5 Supervised Learning Research about learning methods Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Unsupervised Learning Research about learning methods Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Deep Networks and Deep Learning Research about deep learning Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Preparing for the exam Ölçme Yöntemleri:
Yazılı Sınav
9 Introduction to Fuzzy Logic Research about fuzzy logic Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
10 Crisp Sets and Fuzzy Sets Learning about sets Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
11 Genetic Algorithms Learning about genetic algorithms Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
12 Ant Colony Algorithms Research about ant colony behavior Öğretim Yöntemleri:
Anlatım, Soru-Cevap
13 Expert Systems Research about expert systems Öğretim Yöntemleri:
Anlatım, Soru-Cevap
14 Machine Learning Research about machine learning Öğretim Yöntemleri:
Anlatım, Soru-Cevap
15 Applications of Artificial Intelligence Researching programming languages used in artificial intelligence applications Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Bireysel Çalışma
16 Term Exams Exam preparation Ölçme Yöntemleri:
Yazılı Sınav, Ödev
17 Term Exams Exam preparation Ölçme Yöntemleri:
Yazılı Sınav, Ödev


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 2 28
Out of Class Study (Preliminary Work, Practice) 14 2 28
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 6 6
Final Exam 1 16 16
Total Workload (Hour) 78
Total Workload / 25 (h) 3,12
ECTS 3 ECTS

Update Time: 21.11.2022 12:22