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Description
The purpose of this module is to provide students with critical skills required for the treatment and advanced analysis of spatial datasets. During the first part of the course, the concept and analysis workflow of supervised machine learning are introduced. Two types of classic models for supervised learning will be taught in depth, including tree-based models (decision tree, random forests, and gradient boosting trees) and artificial neural network. The bias-variance analysis of machine learning models will also be introduced. The second part of the course moves towards the principle and classic techniques of causal inference. Students will learn how to formulate and tackle causal inference problems, with applications in spatial research. The third part of the course will focus on unsupervised machine learning, including dimensionaliy reduction and clustering analysis. Contents covered include: applied machine learning, regression, classification, decision tree, random forest, neural network, panel regression, difference in difference, regression discontinuity, clustering, dimensionality reduction. Prerequisites of this module include basic Python programming and a conceptual understanding of linear regression models.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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