| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| ECO2868 | Machine Learning with R | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | Tr |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. OZAN BAKIŞ |
| Course Objectives: | This course aims to teach basic algorithms used in Machine Learning using R programming language. The course is based on learning by doing principle. |
|
The students who have succeeded in this course; 1. Basics and uses of machine learning 2. Regression analysis 3. Classification 4. Clustering 5. Model choice 6. Forecasting and measuring forecast performance. |
| After introducing key concepts of machine learning and discussing the relationship of machine learning with statistics and data science, basic algorithms used in machine learning will be taught using a learning by doing approach. For this reason, students will be encouraged to try themselves, make mistakes and learn from their mistakes. Students are expected to apply what they have learned by working on a data and topic that they will choose together with the teacher of the course in groups of 2-3 people. The final exam will be in the form of oral examination where students present the project that has been worked on throughout the semester. There is no requirement for this course but familiarity with R programming language and basic statistical concepts will be assumed. |
| Week | Subject | Related Preparation | |
| 1) | Introduction | ISLR, Ch.1,2 | |
| 2) | R reminder | ||
| 3) | Multiple regression analysis | ISLR, Ch. 3 | |
| 4) | Classification: logistics regerssion and discriminant analysis | ISLR, Ch. 4 | |
| 5) | Classification: naive Bayes and k-nearest neighbour (kNN) | ISLR, Ch. 4 | |
| 6) | Classification: decision trees | ISLR, Ch. 8 | |
| 7) | Forecast performance and model selection | ISLR, Ch. 5, 6 | |
| 8) | Forecast performance and model selection | ISLR, Ch. 5, 6 | |
| 9) | Midterm | ||
| 10) | Principal componenet analysis (PCA) | ISLR, Ch. 12 | |
| 11) | Clustering: k-means | ISLR, Ch. 12 | |
| 12) | Clustering: hierarchical clustering | ISLR, Ch. 12 | |
| 13) | Preparation for the project presentation | ||
| 14) | Project presentation | ||
| Course Notes: | |
| References: | James, G., D. Witten, T. Hastie, and R. Tibshirani (2021). An Introduction to Statistical Learning, with Applications in R, https://www.statlearning.com |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 10 |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 40 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 50 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 30 | 30 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 30 | 30 |
| Total Workload | 144 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |