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mech-m-dual-2-mlb

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for Machine Learning in Industrial Image Processing. All the topics are presented with the Python implementation included.

https://github.com/kandolfp/mech-m-dual-2-mlb

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for Machine Learning in Industrial Image Processing. All the topics are presented with the Python implementation included.

Basic Info
  • Host: GitHub
  • Owner: kandolfp
  • License: other
  • Language: TeX
  • Default Branch: main
  • Size: 23.9 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed 10 months ago
Metadata Files
Readme License Citation Zenodo

README.md

MECH-M-DUAL-2-MLB - Maschinelles Lernen in der industriellen Bildverarbeitung

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for Machine Learning in Industrial Image Processing. All the topics are presented with the Python implementation included as well as examples for self study sessions. We cover clustering and classification (supervised and unsupervised), basic concepts of data management and data engineering (including dvc for model version control), neural networks (the basics, CNN, Autoencoders, Transfer learning) and some further topics.

Citing this project

Citation information

DOI

Development

We use Quarto to generate the lecture material. Where we are creating a book, see docs for the structure. In short, each part has its own folder where you find the qmd files and everything is managed via _quarto.yml. In order to make the use easy the entire project is managed with pdm so to start the preview run

bash pdm sync pdm quarto preview

The project is also compatible with the VSCode extension of Quarto, just make sure the the Python environment in ./.venv is used.

Publishing

After pushing the published website will automatically be built and deployed at kandolfp.github.io/MECH-M-DUAL-2-MLB/. Due to the dynamic nature of the material this might take a couple of minutes.

You can also create a pdf by calling pdm run quarto render --to pdf

or the html version pdm run quarto render --to html

You can also find a pdf in the releases

Owner

  • Login: kandolfp
  • Kind: user

Citation (CITATION.cff) 

title: MECH-M-DUAL-2-MLB - Machine Learning in Industrial Image Processing
abstract: >-
  Course material for a ~3 * 15 hours (5 ECTS) course on 
  basic concepts for Machine Learning in Industrial Image Processing. 
  All the topics are presented with the Python implementation included
  as well as examples for self study sessions. We cover 
  clustering and classification (supervised and unsupervised), 
  basic concepts of data management and data engineering 
  (including dvc for model version control), neural networks 
  (the basics, CNN, Autoencoders, Transfer learning) and some further topics.
authors:
- family-names: Kandolf
  given-names: Peter
  orcid: 'https://orcid.org/0000-0003-3601-0852'
cff-version: 1.2.0
identifiers:
  - type: doi
    value: 10.5281/zenodo.16319881
keywords:
- image processing
- neural networks
- data management
- classification
- machine learning
- data science
license: CC-BY-NC-SA-4.0
version: v1.0.0
date-released: '2025-07-22'

GitHub Events

Total
  • Create event: 1
  • Release event: 1
  • Issues event: 31
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 9
  • Member event: 1
  • Push event: 86
Last Year
  • Create event: 1
  • Release event: 1
  • Issues event: 31
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 9
  • Member event: 1
  • Push event: 86

Dependencies

.github/workflows/publish.yml actions
  • JamesIves/github-pages-deploy-action v4 composite
  • actions/checkout v4 composite
  • pdm-project/setup-pdm v4 composite
  • softprops/action-gh-release v2 composite
pyproject.toml pypi
  • DateTime >=5.5
  • imageio >=2.35.1
  • jupyter >=1.1.1
  • matplotlib >=3.9.2
  • netCDF4 >=1.7.2
  • odfpy >=1.4.1
  • owid-catalog >=0.3.11
  • pandas >=2.2.3
  • plotly >=5.24.1
  • pyqt5 >=5.15.11
  • python-sensors >=0.3.5
  • pywavelets >=1.7.0
  • quarto-cli >=1.5.57
  • scikit-learn >=1.5.2
  • scipy >=1.14.1
  • seaborn >=0.13.2
  • yq >=3.4.3