Course Description

Introduction to machine learning with a focus on applying ML techniques to problems in GIS and remote sensing. Topics to include regression, neural networks and deep learning, kernel methods, and clustering algorithms. Emphasis to be placed on geospatial analytics working with real data sets from practical applications such as crime and disease mapping, data fusion and image analysis, water quality and yield prediction.

Course Outline

  • What is Machine Learning?
  • Regression
  • Nearest Neighbors
  • Support Vector Machines
  • Probabilistic Models
  • Ensemble Methods
  • Clustering Algorithms
  • Neural Networks and Deep Learning
  • Decision Trees and Random Forests

Learner Outcomes

  • Select a machine learning model and algorithm appropriate for a given problem;
  • Formulate an appropriate evaluation scheme in order to tune model parameters and measure the quality of results;
  • Apply machine learning techniques to real data sets to solve a problem in a chosen domain, and interpret the results.


GEO5090 - Introduction to Programming for GIS/Remote Sensing or equivalent experience


30 Hours | 5 Days or 10 Nights

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