The objective of the course is to introduce students to fundamental machine learning methods with a clear focus on their application in applied economics. Students will learn how to use these methods to analyze real-world economic problems, such as forecasting prices, demand, and macroeconomic indicators, as well as studying consumer behavior and policy effects.

Special emphasis is placed on working with real economic datasets, interpreting results in an economic context, and linking machine learning approaches with standard econometric methods. Upon completion, students will be able to independently conduct empirical economic analysis using machine learning techniques, develop predictive models, and communicate results effectively for economic decision-making and business applications.

1. Application of machine learning to economic problems (prediction, classification, pattern detection) and links to econometric approaches.
2. Working with economic data (panel data, time series, micro-level data) and data preparation.
3. Predictive modeling of key economic variables (e.g. prices, demand, employment).
4. Nonlinear methods (decision trees, random forests) for modeling complex economic relationships.
5. Dimensionality reduction and clustering for market segmentation and consumer analysis.
6. Machine learning for economic time series (e.g. inflation or fuel price forecasting).
7. Interpretation of results in an economic context and comparison with traditional methods.
8. Empirical case studies (fuel prices, policy evaluation, consumer behavior) and a project using real-world data.