---
tags: courses
title: GeoML
---
# Practical machine learning with spatial data

:::info
**This document:** https://hackmd.io/@GeospatialCSC/GeoML
**[Slides](https://drive.google.com/drive/folders/1q0-eSCFKcApzTql828Z2ZfDe8xFeFjXd?usp=sharing)**
**[Exercise material](https://github.com/csc-training/GeoML)**
**Computing environment:** www.puhti.csc.fi
:::

## Schedule
:::spoiler Monday 7.11.
**9:00-12:00**
* Practicalities and 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
:::

:::spoiler Tuesday 8.11.

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

:::

:::spoiler Wednesday 9.11.
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
:::

## Practicalities

-> https://hackmd.io/@GeospatialCSC/GeoML/README.md