Electrical and Computer Engineering Graduate With Thesis
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 80511159 | Augmented Reality and Applications | 2023 | Autumn | 1 | 3+0+0 | 7,5 | 7,5 |
| Course Type | Elective |
| Course Cycle | Master's (Second Cycle) (TQF-HE: Level 7 / QF-EHEA: Level 2 / EQF-LLL: Level 7) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Lect. Zarina OFLAZ |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Providing students with theoretical and applied information on Data Analysis and Machine Learning is aimed.
Objectives of the Course
Providing students with theoretical and applied information on Data Analysis and Machine Learning is aimed.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Data science and its application to the real-world problems |
| 2 | The process of Data Science, types of the underlying fields, models |
| 3 | Veri Analizi ve veri analizin süreci |
| 4 | Descriptive Analysis |
| 5 | Introduction to R programming, basics of programming |
| 6 | R'da .csv,.txt formatlarında veri hazırlama ve aktarma |
| 7 | Descriptive analysis, graphical analysis |
| 8 | The modeling process of Machine Learning and Statistical Modelling |
| 9 | Makine Öğrenimi nedir? Makine Öğrenimi modellerin türleri ve amaçları |
| 10 | Supervised Learning: classification and regression |
| 11 | Classification and Regression Trees (CART) |
| 12 | R'da karar ağacı modelin uygulanması |
| 13 | Intrepretation of the CART model result |
| 14 | Case study |
Textbook or Material
| Resources | notes |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||