Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05071260 | Machine Learning | 4 | Autumn | 7 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Ali Osman ÇIBIKDİKEN | A-124 | [email protected] | 7585 | Monday 14.00-15.00 |
Course Content
Course Content: Lesson 1: Introduction to Machine Learning. Lesson 2: Understanding Data Through Descriptive Statistics. (Analyzing Data) Lesson 3: Understanding Data Through Visualization. (Analyzing Data) Lesson 4: Pre-Processing Data. (Preparing Data) Lesson 5: Feature Selection. (Preparing Data) Lesson 6: Resampling Methods. (Evaluating Algorithms) Lesson 7: Algorithm Evaluation Metrics. (Evaluating Algorithms) Lesson 8: Spot-Checking Classification Algorithms. (Evaluating Algorithms) Lesson 9: Spot-Checking Regression Algorithms. (Evaluating Algorithms) Lesson 10: Model Selection. (Evaluating Algorithms) Lesson 11: Pipelines. (Evaluating Algorithms) Lesson 12: Ensemble Methods. (Improving Results) Lesson 13: Algorithm Parameter Tuning. (Improving Results) Lesson 14: Model Finalization. (Presenting Results)
Objectives of the Course
Bu makine öğrenmesi dersi, öğrencilere temel makine öğrenimi kavramlarını ve tekniklerini sunmayı amaçlar. Veri analizi, hazırlama, algoritma değerlendirmesi ve sonuç sunumu gibi adımları kapsar. Öğrenciler, veriyi anlama, önişleme, özellik seçimi, yeniden örnekleme gibi beceriler kazanır. Ayrıca sınıflandırma, regresyon algoritmalarını incelemek, ensemble yöntemleri ve parametre ayarlama gibi ileri konuları öğrenmek de dersin hedeflerindendir. Bu ders, öğrencilere makine öğrenimi alanında sağlam temel bilgi ve yetenekler kazandırmayı amaçlar.
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 |
|---|---|---|
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Predictive modeling | P.2.21 | 1,7 |
| O2 | Data analysis | P.2.22 | 1,7 |
| O3 | Algorithm | P.2.23 | 1,7 |
| ** 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 | Lesson 1: Introduction to Machine Learning. |
| 2 | Lesson 2: Understanding Data Through Descriptive Statistics. (Analyzing Data) |
| 3 | Lesson 3: Understanding Data Through Visualization. (Analyzing Data) |
| 4 | Lesson 4: Pre-Processing Data. (Preparing Data) |
| 5 | Lesson 5: Feature Selection. (Preparing Data) |
| 6 | Lesson 6: Resampling Methods. (Evaluating Algorithms) |
| 7 | Lesson 7: Algorithm Evaluation Metrics. (Evaluating Algorithms) |
| 8 | Lesson 8: Spot-Checking Classification Algorithms. (Evaluating Algorithms) |
| 9 | Lesson 9: Spot-Checking Regression Algorithms. (Evaluating Algorithms) |
| 10 | Lesson 10: Model Selection. (Evaluating Algorithms) |
| 11 | Lesson 11: Pipelines. (Evaluating Algorithms) |
| 12 | Lesson 12: Ensemble Methods. (Improving Results) |
| 13 | Lesson 13: Algorithm Parameter Tuning. (Improving Results) |
| 14 | Lesson 14: Model Finalization. (Presenting Results) |
Textbook or Material
| Resources | 1. Mitchell, T.M., Machine learning. 1997. |
| 1. Mitchell, T.M., Machine learning. 1997. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 3 | 3 |
| 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 | 1 | 3 | 3 |
| Other | 14 | 4 | 56 |
| Total Work Load: | 160 | ||
| Total Work Load / 30 | 5,33 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 |
|---|---|---|
| O1 | Predictive modeling | 3 |
| O2 | Data analysis | 5 |
| O3 | Algorithm | 2 |