Training course on Introduction to Machine Learning for Central Bank Economists

Module 1: Introduction to Machine Learning for Economists

What is machine learning?

Differences between traditional econometrics and ML

Applications in central bank research and supervision

Module 2: Data Foundations for ML

Structured vs. unstructured data in central banking

Data cleaning, preprocessing, and feature engineering

Handling missing data and high-dimensional datasets

Module 3: Supervised Learning Methods

Regression models (linear, logistic, ridge, lasso)

Decision trees and random forests

Applications: inflation forecasting, credit scoring

Module 4: Unsupervised Learning Methods

Clustering techniques (k-means, hierarchical clustering)

Dimensionality reduction (PCA, t-SNE)

Applications: financial market segmentation, detecting anomalies

Module 5: Time Series and Forecasting with ML

Comparison of econometric and ML-based forecasting

ML models for macroeconomic variables

Hybrid models (econometrics + ML)

Module 6: Natural Language Processing (NLP) for Central Banks

Text mining of financial news and central bank reports

Sentiment analysis for financial stability monitoring

Topic modeling for research insights

Module 7: Model Evaluation and Validation

Training, testing, and cross-validation

Performance metrics (accuracy, precision, recall, RMSE)

Avoiding overfitting and ensuring robustness

Module 8: Practical Tools and Software

Introduction to Python/R for ML (pandas, scikit-learn, TensorFlow basics)

Building and testing simple models

Visualization and interpretation of results

Module 9: Machine Learning in Central Bank Applications

Forecasting inflation and GDP

Stress testing and systemic risk detection

Supervisory technology (SupTech) and regulatory technology (RegTech)

Module 10: Ethics, Governance, and Limitations of ML

Interpretability and transparency in ML models

Bias and fairness in algorithms

Central banks’ responsibility in adopting ML

Module 11: Case Studies and Global Practices

ML use in ECB, Bank of England, Federal Reserve, and other central banks

Lessons from emerging markets adopting AI/ML

Real-world project examples

Module 12: Capstone Project

Build a simple ML model using central bank data (inflation, credit, or financial stability)

Present results, insights, and policy implications

Peer and instructor feedback