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Practical machine learning with spatial data

7.11.2022 09:00 +02:00 EET - 9.11.2022 16:00 +02:00 EET

CSC Training Facilities & Online (Zoom)

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.

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.

The course is similar to the Practical machine learning for spatial data course kept in autumn 2019 and 2020. Course exercise materials of previous course are available in Github: https://github.com/csc-training/geocomputing/tree/master/machineLearning

The new materials will be published here later.

Course organizers and lecturers: Kylli Ek, Samantha Wittke, Billy Braithwaite, Markus Koskela, Mats Sjöberg (all CSC)

Preliminary program

Monday 7.11.2022

9:00-12:00

  • Introduction
  • Lecture 1: Introduction to machine learning
  • Exercise 1: Image segmentation using k-means with scikit-learn

12.00-13:00

Lunch break

13:00-16:15

  • Lecture 2: Shallow machine learning models
  • Lecture 3: Preparing spatial data for machine learning
  • Exercise 2: Preparing vector data for regression

Tuesday 8.11.2022

9:00-12:00

  • Exercise 3: Shallow regression with scikit-learn
  • Exercise 4: Image classification using shallow classifiers, grid search with scikit-learn
  • Lecture 4: Introduction to deep learning models

 

12.00-13:00

Lunch break

13:00-16:15

  • Lecture 5: Fully connected neural networks
  • Lecture 6: Puhti GPUs and batch jobs
  • Exercise 5: Fully connected regressor with keras

Wednesday 9.11.2022

9:00-12:00

  • Exercise 6: Fully connected classifier with keras
  • Lecture 7: Convolutional neural networks (CNN)
  • Exercise 7: Data preparations for CNN

12.00-13:00

Lunch break

13:00-16:00

  • Exercise 8: CNN based image segmentation with keras
  • Lecture 8: GIS software supporting machine learning for spatial data. 
  • Wrap-up and where to go from here

We will have coffee breaks also during morning and afternoon sessions. Participants at CSC are provided lunch and refreshments during coffee breaks.

Exercise set-up

All exercises will be done in CSCs supercomputer Puhti, using the Puhti web interface. 

All participants will get Puhti training accounts.

Participants at CSC

The course is organized in CSC training class room, where everybody has access to a training PC. 

Online participants

Technical requirements, minimum:

  • Zoom.
  • Web browser.

Optional:

  • ArcGIS or QGIS for viewing the spatial data files (GeoTiff, Shape, GeoPackage).
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Sale ends 4.11.2022 23:59

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Sale ends 4.11.2022 23:59

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Event time

Starts:  

7.11.2022 09:00 +02:00 EET

Ends:  

9.11.2022 16:00 +02:00 EET

Event location

CSC Training Facilities & Online (Zoom)

Keilaranta 14
02100 Espoo

View larger map and directions

Attendance fee:

Finnish University or institution of higher education
223,20€ (incl. VAT 24%)


Finnish state research institutions or government organizations
223,20€ (incl. VAT 24%)

Other

 

Organizer

Kylli Ek

giscoord@csc.fi