ECO-5108 : Machine learning and spatial data analysis
Responsible teacher: Mathias SILVA VAZQUEZ (mathias.silva_vazquez [at] ens-lyon.fr)
This course introduces a range of modern research techniques to deal with high dimensional and potentially unstructured data. These techniques fall into two distinct categories. The first part of the course introduces statistical learning techniques. In recent years, there has been an increasing interest in predictive modelling, in particular for the collection of original research data that would not be available using traditional methods. This course provides a comprehensive understanding of some of the most capable supervised learning algorithms, including GAMs, random forests, boosted trees, or neural networks. The second part of the course focuses on applied spatial economics. Many economic phenomena are spatial in nature. Manipulating and analysing spatial data rely on specific sets of tools known as geographical information systems. Specifically, students will learn how to manage vector and raster data, perform geo-computations, represent spatial processes and fit spatial models. This course contains comprehensive theorising and mathematical formalisation but keeps a strong focus on intuition and effective implementation. In particular, we make extensive use the R programming language, both to illustrate abstract statistical concepts using simulated data, and to perform economic analysis on actual datasets.
This course helps you develop a solid theoretical background in machine learning, as well as the ability to understand and implement a variety of machine learning models. Besides, you will acquire a good understanding of spatial data formats and spatial computations, that will enable you to analyse spatial economic phenomenon. Finally, you will achieve greater proficiency in the R language, which enable you to handle data and estimate models with great flexibility.
