Practical Applications of AI and Machine Learning in Economic Modeling:

This session focuses on applied uses of AI and ML in economic modeling. Participants will examine how these techniques can be employed to model economic variables such as production, employment, inflation, and GDP. The session highlights the development of robust and interpretable models using regression, classification, and dimensionality reduction techniques, with practical applications in market analysis, economic risk evaluation, and policy modeling.

1. Foundations and Evolution of Artificial Intelligence in Economic Modeling

This topic provides an overview of the fundamental principles underlying Artificial Intelligence and examines its expanding role in economic modeling. Participants will gain insight into recent advancements in AI and Machine Learning and how these developments are reshaping contemporary approaches to economic analysis and model construction.

2. Python as a Tool for Economic Machine Learning Models

This session focuses on the use of Python as a practical tool for implementing machine learning models in economic contexts. Participants will develop core programming skills, learn techniques for preparing and managing economic data, and apply selected machine learning algorithms commonly used in AI-driven economic modeling.

3. Machine Learning Methods for Enhancing Economic Model Performance

This topic explores how machine learning methods contribute to improving the robustness and adaptability of economic models. Participants will learn how ML techniques can process large and complex datasets, capture underlying economic relationships, and support the modeling of essential indicators such as GDP growth, inflation dynamics, and interest rates.

4. Data-Driven Machine Learning Techniques for Economic Modeling

This session presents key machine learning techniques used in data-driven economic modeling. Participants will examine supervised learning approaches, including regression and classification, and understand their role in modeling economic relationships and improving predictive accuracy. The session also covers unsupervised learning methods, such as clustering and dimensionality reduction, highlighting their use in analyzing economic data, identifying structural patterns, and supporting exploratory economic analysis.