Applied Economics with Machine Learning

Aims of the course

The objective of this course is to introduce students to the fundamentals of applied economics and machine learning techniques. Students will learn how to apply these techniques to real-world economic problems and make data-driven decisions. Upon completing the course, students will have the competencies to analyze economic data, build predictive models, and communicate findings effectively.

Course syllabus

1. Introduction to Machine Learning in Economics
2. Data Analysis and Visualization: exploring distribution of income across different regions and demographic groups
3. Regression Analysis: estimating the impact of socio-economic characteristics on earnings
4. Model Selection and Regularization: comparing the performance of different regression models for predicting prices
5. Clustering and Dimension Reduction: clustering customers based on their buying behavior to improve marketing strategies
6. Decision Trees and Random Forests: predicting whether a customer is likely to churn
7. Neural Networks and Deep Learning: predicting real estate prices based on various characteristics of the property
1. Time Series Analysis and Forecasting: building a long short memory (LSTM) model to predict electricity demand
2. Causal Inference and Counterfactual Analysis: estimating the impact of a policy measure on the employment rate
3. Natural Language Processing and Text Analysis: analyzing the competition and market positioning based on media reports
4. Applications of Machine Learning in Economics: creating a business project using concepts and techniques learned throughout the course.

Course director(s)