Teaching

This course introduces machine learning for quantitative research in economics. Starting  with non-linear regression, the course provides a comprehensive understanding of some of the most capable supervised learning models such as random forest, gradient boosted trees and neural networks, including specialised model structures for modelling images and text data. Every model is solved mathematically before being implemented and optimised from scratch using Python. Practical applications focus on predictive modelling in economics including the processing of high-dimensional data such as satellite images and historical maps, documents, news transcripts, among others.

Topics coming soon: Autoencoders, Disentangled representations

This course introduces image processing and a range of specialised network structures to extract information form images. These tools enable the researcher to transform high-dimensional and unstructured data such as historical maps, handwritten documents, aerial and satellite images into simpler representations that can be used as inputs to econometric models. The models are solved mathematically, implemented and optimised form scratch in Python.

 Topics coming soon: Deep Q-learning

This course introduces regression methods for applied economic research. After covering multivariate regression and statistical inference, the course focuses on practical estimation issues such as collinearity, heteroscedasticity, serial correlation or endogeneity. Each problem is detected using the appropriate statistics and valid estimates are computed using alternative estimators. Sessions include comprehensive theorising yet keep a strong focus on intuition and effective implementation. We make extensive use the R programming language, both to illustrate abstract statistical concepts using simulated data, and to replicate research papers.