BİLİŞİM GÜVENLİĞİ TEKNOLOJİSİ | |||||
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Qualification Awarded | Program Süresi | Toplam Kredi (AKTS) | Öğretim Şekli | Yeterliliğin Düzeyi ve Öğrenme Alanı | |
2 | 120 | FULL TIME |
TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 5 QF-EHEA:Short Cycle TR-NQF-HE, ISCED (1997-2013): 48,52 |
Course Code: | 2000002013 | ||||||||||
Ders İsmi: | Artificial Intelligence | ||||||||||
Ders Yarıyılı: | Spring | ||||||||||
Ders Kredileri: |
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Language of instruction: | TR | ||||||||||
Ders Koşulu: | |||||||||||
Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||||
Other Recommended Topics for the Course: | |||||||||||
Type of course: | Bölüm Seçmeli | ||||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||||
Course Coordinator : | Öğr.Gör. Serhat DALGALIDERE | ||||||||||
Course Lecturer(s): |
Öğr.Gör. Serhat DALGALIDERE |
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Course Assistants: |
Course Objectives: | The aim of this course is to provide students with an introduction to artificial intelligence, including the basic techniques and mechanisms of artificial intelligence. It is aimed that the students who complete this course will understand the historical and conceptual development of artificial intelligence, the aims of artificial intelligence and the methods it uses to achieve these goals, the social and economic role of artificial intelligence, and by analyzing the problems, determining where artificial intelligence techniques can be used and using artificial intelligence techniques. |
Course Content: | Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Search methods, Planning, Heuristic Problem Solving, Information representation, Predicate Logic, Artificial Intelligence Programming Languages, Programming with Common Lisp, Game Theory, Genetic Algorithms, Fuzzy Logic, Expert Systems, Artificial Intelligence Applications. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Meeting students and explaining the content of the course. | |
2) | Artificial Intelligence Definition, History, Development and Application Areas | |
3) | Data Mining, Steps of the Knowledge Discovery Process in Data Mining and Its Relationship with Other Fields. | |
4) | What is Machine Learning? Machine Learning Methods | |
5) | Classification, Basic Concepts of Classification. | |
6) | Decision Trees, Separation and Stopping Criteria in Decision Trees | |
7) | Decision Tree Algorithms | |
8) | Midterm Exam-1 | |
9) | Support Vector Machines | |
10) | Logistic Regression, Logistic Regression Types | |
11) | Bayes Classifiers, Definition of Probability, Basic Concepts and Probability Axioms | |
12) | Midterm Exam-2 | |
13) | K Nearest Neighbor Algorithm | |
14) | Clustering Analysis, Stages of Clustering Analysis and Clustering Methods | |
15) | Fınal Exam |
Course Notes / Textbooks: | Ders Notları |
References: | 1.Vasif Nabiyev, Yapay Zeka, 5. Baskı, Seçkin Yayınevi 2.Yapay Zeka Ders Notu, Cahit Karakuş, 2023. https://ckk.com.tr/ders/YZ/YZ%2000%20Yapay%20Zeka%20Ders%20Notu.html 3. Makine Öğrenmesinde Sınıflandırma Yöntemleri ve R Uygulamaları, Selçuk Alp, Ersoy Öz., Nobel Akademik Yayıncılık, 2019. |
Ders Öğrenme Kazanımları | 1 |
2 |
3 |
4 |
5 |
6 |
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Program Outcomes | ||||||
1) Having knowledge and skills in security algorithms for programming | ||||||
2) Ability to install and manage software required for end user security | ||||||
3) Having the ability to install and manage computer networks and use the network operating system | ||||||
4) Have basic database and web programming skills |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Having knowledge and skills in security algorithms for programming | 2 |
2) | Ability to install and manage software required for end user security | 3 |
3) | Having the ability to install and manage computer networks and use the network operating system | 2 |
4) | Have basic database and web programming skills | 2 |
Alan Çalışması | |
Anlatım | |
Bireysel çalışma ve ödevi | |
Course | |
Grup çalışması ve ödevi | |
Labs | |
Homework | |
Problem Çözme | |
Proje Hazırlama | |
Rapor Yazma | |
Rol oynama | |
Soru cevap/ Tartışma | |
Sosyal Faaliyet | |
Örnek olay çalışması | |
Web Tabanlı Öğrenme |
Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
Sözlü sınav | |
Homework | |
Uygulama | |
Gözlem | |
Sunum | |
Bilgisayar Destekli Sunum | |
Örnek olay sunma |
Semester Requirements | Number of Activities | Level of Contribution |
Midterms | 1 | % 40 |
Semester Final Exam | 1 | % 60 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 2 | 28 |
Study Hours Out of Class | 14 | 2 | 28 |
Midterms | 1 | 10 | 10 |
Final | 1 | 10 | 10 |
Total Workload | 76 |