Information Security Technology
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 08121111 | Artificial Intelligence and Its Applications | 1 | Spring | 2 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | Dersin herhangi bir ön koşulu bulunmamaktadır. Tüm öğrencilere temel seviyeden başlanarak eğitim verilmektedir. |
| Coordinator | - |
| Instructor(s) | Lect. Ayşe Merve BÜYÜKBAŞ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Ayşe Merve BÜYÜKBAŞ | C-127 | [email protected] | 7436 | Wednesday 10:00-12:00 |
Course Content
This course includes; What is artificial intelligence? History and basic concepts, Machine learning fundamentals: supervised and unsupervised learning, Deep learning and artificial neural networks, Convolutional neural networks (CNN), Natural language processing (NLP) and transformer models, Image processing and computer vision, Big data, computational power and AI systems, Artificial intelligence and its applications, Artificial intelligence in specialized fields (medicine, games, etc.), Fuzzy logic, Evolutionary algorithms and optimization, Benefits and risks of artificial intelligence, ethical issues and concerns, societal impact of artificial intelligence, and student presentations on selected topics and applications.
Objectives of the Course
The aim of the Artificial Intelligence and Applications course is to teach students the concepts, methods, and application areas of artificial intelligence; and to provide them with the ability to apply AI techniques to real-world problems.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | He/she has basic, current and practical knowledge about his/her profession. | 5 |
| P3 | Follows current developments and practices for his/her profession and uses them effectively. | 4 |
| P4 | Uses professionally relevant information technologies (software, programs, animations, etc.) effectively. | 5 |
| P5 | Has the ability to independently evaluate professional problems and issues with an analytical and critical approach and to propose solutions. | 3 |
| P6 | Can effectively present thoughts through written and verbal communication at the level of knowledge and skills and express them in an understandable manner. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | P.1.6 | 1,2,5 | |
| O2 | P.3.2 | 6,7 | |
| O3 | P.3.5 | 5 | |
| O4 | P.4.3 | 3,6,7 | |
| O5 | P.5.4 | 1,2,5 | |
| O6 | P.6.2 | 2,5 | |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | What is artificial intelligence? History and basic concepts |
| 2 | Machine learning fundamentals: supervised and unsupervised learning |
| 3 | Deep learning and artificial neural networks |
| 4 | Convolutional neural networks (CNN) |
| 5 | Natural language processing (NLP) and transformer models |
| 6 | Image processing and computer vision |
| 7 | Big data, computing power, and AI systems |
| 8 | Midterm Exam |
| 9 | Artificial intelligence and its applications |
| 10 | Artificial intelligence in specialized fields (medicine, games, etc.) |
| 11 | Fuzzy logic |
| 12 | Evolutionary algorithms and optimization |
| 13 | Benefits and risks of artificial intelligence, ethical issues and concerns, societal impact of artificial intelligence |
| 14 | Student presentations on selected topics and applications |
| 15 | Final Exam |
Textbook or Material
| Resources | Notes shared by the course instructor |
| Artificial Intelligence in 50 Questions, Cem Say Science and Future Library |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 8 | 112 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 1 | 14 | 14 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 1 | 4 | 4 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P3 | P4 | P5 | P6 |
|---|---|---|---|---|---|---|
| O1 | Öğrenilen bilgileri uygulamalı örneklerle gösterir. | - | - | - | - | - |
| O2 | Güncel yazılım araçlarını uygular. | - | - | - | - | - |
| O3 | Mesleki yenilikleri meslektaşlarına aktarır. | - | - | - | - | - |
| O4 | Algoritma geliştirmeyi bilir ve algoritmaya uygun veri yapısı oluşturur. | - | - | - | - | - |
| O5 | Alternatif çözüm yollarını değerlendirir ve en uygun olanını seçer. | - | - | - | - | - |
| O6 | Topluluk önünde kendinden emin bir şekilde konuşur ve sunum yapar. | - | - | - | - | - |