Welcome...
This course gives a practical introduction to machine learning with spatial data, both to shallow learning and deep learning models, including convolutional neural networks (CNN).
The course consists of lectures and hands-on exercises in Python. We will use scikit-learn for the shallow learning exercises and keras for deep learning exercises.
Learning outcome
After the course the participants should have the skills and knowledge needed to start applying machine learning for different spatial data analysis tasks. In addition, participants will be able to makes use of the GPU resources available at CSC High Performance Computers for training and deploying their own machine learning models.
After the course the participants should have the skills and knowledge needed to start applying machine learning for different spatial data analysis tasks. In addition, participants will be able to makes use of the GPU resources available at CSC High Performance Computers for training and deploying their own machine learning models.
Prerequisites
- Basics of geoinformatics, vector and raster data, coordinate systems.
- Basics of Python. The course will include a fair amount of reading Python code, so you should be able to follow Python syntax. If you need to refresh your Python skills you can go through the materials of Helsinki University GeoPython course.
- Basic Linux commands: cd, ls, mv, cp, rm, chmod, less, tail, echo, mkdir, pwd. If unfamiliar, take a look for example at LinuxSurvival first two modules.
Course exercises
- Course slides are available only for Finnish universities (HAKA login required)
- Course exercise materials of
are available for everybody in Github.