BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ECONOMICS | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| ECO2868 | Machine Learning with R | Fall | 3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | Turkish |
| Type of course: | Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| 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. |
Learning Outcomes
|
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. |
Course Content
| 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. |
Weekly Detailed Course Contents
| 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 |
Sources
| Course Notes / Textbooks: | |
| References: | James, G., D. Witten, T. Hastie, and R. Tibshirani (2021). An Introduction to Statistical Learning, with Applications in R, https://www.statlearning.com |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 10 |
| Midterms | 1 | % 40 |
| Final | 1 | % 50 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Midterms | 1 | 30 | 30 |
| Final | 1 | 30 | 30 |
| Total Workload | 144 | ||
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | As a world citizen, she is aware of global economic, political, social and ecological developments and trends. | 2 |
| 2) | He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning. | 5 |
| 3) | Absorbs basic economic principles and analysis methods and uses them to evaluate daily events. | 4 |
| 4) | Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders. | 5 |
| 5) | Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions. | 3 |
| 6) | Comprehends new ways of doing business using digital technologies. and new market structures. | 2 |
| 7) | Takes critical approach to economic and social problems and develops analytical solutions. | 1 |
| 8) | Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods. | 3 |
| 9) | In the works he/she contributes, observes individual and social welfare together and with an ethical perspective. | 1 |
| 10) | Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines. | 3 |
| 11) | Generates original and innovative ideas in the works she/he contributes as part of a team. | 5 |