School of Economics and Administrative Sciences \ Economics
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
This course covers artificial intelligence and machine learning methods and applications commonly used in economics and finance. It focuses on various supervised learning techniques, regression, and classification methods within modern statistical learning and inferential modeling. Additionally, in the context of unsupervised learning, approaches such as principal component analysis and clustering (hierarchical, k-means) are examined.
Textbook and / or References
1. An Introduction to Statistical Learning, G. James & D. Witten & T. Hastie & R. Tibshirani, Updated 2nd edition, 2023.
2. An Introduction to R, W.N. Venables & D.M. Smith & R Core Team.
The objective of this course is to bridge the gap between statistics/econometrics and machine learning by introducing fundamental statistical learning concepts. In this regard, contemporary methods and tools necessary for analyzing large datasets, particularly in economics and finance, are covered, and students gain hands-on experience with real-world data.
1. Acquire the necessary mathematical knowledge for economic analysis.
2. Develop advanced skills in statistics, econometrics, and software tools for analyzing and interpreting economic data.
3. Gain interdisciplinary knowledge to adopt a multidimensional approach to economic analysis.
Week 1: Introduction
Week 2: Basic Concepts
Week 3: Bias-Variance Tradeoff
Week 4: Linear Regression
Week 5: Practical Issues
Week 6: Classification Analysis
Week 7: Discriminant Analysis
Week 8: Cross-Validation
Week 9: Bootstrapping
Week 10: Decision Trees
Week 11: Bagging, Boosting, Random Forests
Week 12: Support Vector Machines
Tentative Assesment Methods
• Homeworks 50 %
• Final 50 %
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