Machine Learning for Geospatial Analysis

Tools and techniques of machine learning applied to geospatial analysis.

Instructor

Dr. Max Anjos

Visiting Professor, Federal University of Rio Grande do Norte, Brazil.

Researcher, Chair of Climatology, Institute of Ecology, Technische Universität Berlin.

Introduction

Welcome to the world of Machine Learning for Geospatial Analysis! In this course, we will explore the fundamental concepts, tools, and techniques of Machine Learning as applied to spatial analysis. Whether you're a novice or have some prior experience, this course will provide you with valuable insights into data manipulation, spatial data exploration, and the creation of predictive models. We will also dive into cluster analysis and dimensionality reduction techniques, all while utilizing the R programming language and Geographic Information Systems (GIS).

Course Highlights

Hands-On Learning: This course takes a practical, hands-on approach to ensure you gain an intuitive understanding of Machine Learning through real-world examples and case studies.

Comprehensive Curriculum: We've carefully structured the course into four main parts to cover a wide range of topics. From the fundamentals of Machine Learning such as, supervised and non-supervised approach, to mastering the R language, you'll be equipped with the skills needed to analyze your dataset or build your own Machine Learning models.

Project-Based: As part of your learning journey, you'll have the opportunity to work on individual Machine Learning projects. This practical experience will allow you to apply what you've learned in a real-world context.

Prerequisites

To make the most of this course, we recommend having:

Basic Data Analysis Skills: Familiarity with tools like Excel will be helpful as we delve into more advanced concepts.

GIS Knowledge: A basic understanding of Geographic Information Systems (GIS) will give you an edge in grasping geospatial concepts.

Statistical Foundation: While not mandatory, a basic knowledge of statistics will be beneficial as we explore various Machine Learning techniques.

Target Audience

This course is designed for both undergraduate and postgraduate students who are eager to explore the intersection of Machine Learning and geospatial analysis. Whether you're pursuing a career in data science, geography, environmental science, or related fields, this course will empower you with valuable skills and knowledge to excel in your endeavors.

First experience: Departament of Geography, Federal University of Rio Grande do Norte - UFRN, Brazil.

Participants are applying the technique of Principal Component Analysis (PCA) for dimensionality reduction in unsupervised learning.