Faculty of Business and Management Sciences · International Trade and Finance (English) · Undergraduate
Course Objective
This course aims to teach students machine learning (ML) algorithms, data modeling techniques, and applications. Students will analyze datasets using supervised and unsupervised learning methods, implement machine learning models using SPSS and Python, and interpret the results.
Course Content
This course covers machine learning algorithms, data analysis techniques, and modeling processes. Students will learn to build supervised and unsupervised learning models using SPSS and Python. Key machine learning techniques such as regression, classification, clustering, dimensionality reduction, neural networks, and natural language processing (NLP) will be explored. Additionally, the course will focus on model evaluation, big data applications in machine learning, and AI ethics.
Required Resources
Alpaydın, Ethem. Makine Öğrenmesi: Algoritmalar ve Uygulamalar. 2. Baskı. İstanbul: Boğaziçi Üniversitesi Yayınları, 2020.
Bishop, Christopher M. Pattern Recognition and Machine Learning. New York: Springer, 2006.
Özdamar, Kazım. SPSS ile Veri Madenciliği ve Makine Öğrenmesi. Eskişehir: Nisan Kitabevi, 2021.
Recommended Resources
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning. 2nd ed. New York: Springer, 2017.
Course Learning Outcomes
- Students will be able to understand the fundamental concepts and algorithms of machine learning and create suitable models for different data types.
- Students will be able to implement machine learning models using SPSS and Python and conduct data analysis processes.
- Students will be able to create classification and prediction models using supervised and unsupervised learning methods.
- Students will be able to develop machine learning solutions for various industries using big data and natural language processing techniques.
- Students will be able to evaluate machine learning models' performance and analyze error rates.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 14 | 2 | 28 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 12 | 3 | 36 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to Machine Learning: Basic Concepts, Algorithm Types | Basic reading, research, analysis. |
| 2 | Data Preprocessing: Missing Data, Normalization, Feature Selection | Basic reading, research, analysis. |
| 3 | Supervised Learning: Regression Analysis (Linear, Logistic) | Basic reading, research, analysis. |
| 4 | Supervised Learning (Classification): Decision Trees, SVM, k-NN | Basic reading, research, analysis. |
| 5 | Unsupervised Learning: Clustering (K-Means, DBSCAN) | Basic reading, research, analysis. |
| 6 | Dimensionality Reduction Techniques: Principal Component Analysis (PCA) | Basic reading, research, analysis. |
| 7 | Neural Networks and Deep Learning: Artificial Neural Networks, Feedforward Networks | Basic reading, research, analysis. |
| 8 | Midterm Exam | - |
| 9 | Natural Language Processing (NLP): Text Mining, Sentiment Analysis | Basic reading, research, analysis. |
| 10 | Recommendation Systems: Content-Based and Collaborative Filtering | Basic reading, research, analysis. |
| 11 | Model Evaluation in Machine Learning: Error Metrics, Cross-Validation | Basic reading, research, analysis. |
| 12 | Big Data and Machine Learning: Hadoop, Spark | Basic reading, research, analysis. |
| 13 | Machine Learning Applications: Finance, Healthcare, Cybersecurity | Basic reading, research, analysis. |
| 14 | Ethical Data Usage and AI Ethics | Basic reading, research, analysis. |
| 15 | Current Trends and Future Perspectives in Machine Learning | Basic reading, research, analysis. |
| 16 | Final Exam | - |