Preface

Introduction

The course “Machine Learning for Socio-Economic and Georeferenced Data” (EPPS6326 & GISC6323) offers a foundational introduction to applying machine learning techniques to the nuanced fields of socio-economic studies and geospatial data analysis. This course is structured to provide students with a basic understanding of how machine learning models and algorithms can be used to analyze complex and dynamic data sets typical in these areas.

Using open-source software and accessible hardware, the course covers essential models and algorithms, grounding them in practical examples to highlight their application in real-world scenarios. The curriculum is designed to demonstrate the utility of both supervised and unsupervised learning techniques in uncovering patterns and making predictions from socio-economic and geospatial data.

Students will have the opportunity to work hands-on with data, applying machine learning techniques to develop an understanding of key concepts such as linear predictions, the bias-variance trade-off, and model performance measures. The course also touches upon the ethical considerations of applying machine learning, encouraging students to think critically about the implications of their work.