Introduction to Machine Learning

Metadata

• Peter Steiner
• AI Applications for Medicine, International Summer School in Dresden, Technische Universität Dresden, Dresden, Saxony, Germany
• Weblink: https://www.secai-ceti-summerschool.de

Summary and Contents

This repository contains code accompanying the workshop entitled "Introduction to Machine Learning". The Jupyter Notebooks are prepared to redo all steps introduced during the workshop.

File list

• The following scripts are provided in this repository
  • scripts/run_jupyter-lab.sh: UNIX Bash script to start the Jupyter Notebook for the workshop.
  • scripts/run_jupyter-lab.bat: Windows batch script to start the Jupyter Notebook for the workshop.
• The following Python code is provided in src
  • src/data/dataset_without_pytorch.py: Utility functions for data handling.
• requirements.txt: Text file containing all required Python modules to be installed.
• README.md: The README displayed here.
• LICENSE: Textfile containing the license for this source code. You can find
• results/
  • (Pre)-trained modelss.
• .gitignore: Command file for Github to ignore files with specific extensions.

Usage

The easiest way to get started is to either use Binder or Colab. Links to open the Jupyter Notebook there are given below.

To run the scripts or to start the Jupyter Notebook locally, at first, please ensure that you have a valid Python distribution installed on your system. Here, at least Python 3.9 is required.

You can then call run_jupyter-lab.ps1 or run_jupyter-lab.sh. This will install a new Python venv, which is our recommended way of getting started.

Acknowledgements

This research was supported by

Nobody

License and Referencing

This program is licensed under the BSD 3-Clause License.

More information about licensing can be found in Wikipedia.

Appendix

For any questions, do not hesitate to open an issue or to drop a line to Peter Steiner