[Review]: Machine Learning Responsible Python

[gperu]

Lesson Title

Responsible machine learning in Python

Lesson Repository URL

https://github.com/carpentries-incubator/machine-learning-responsible-python

Lesson Website URL

https://carpentries-incubator.github.io/machine-learning-responsible-python/

Lesson Description

This lesson explores key topics on the responsible application of machine learning. The lesson is presented as a series of case studies that illustrate real world examples. Sections cover a broad range of topics, including reproducibility, bias, and interpretability. Broadly the topics are ordered chronologically, appearing as they would when thinking through a research study.

Author Usernames

@tompollard

Zenodo DOI

No response

Differences From Existing Lessons

No response

Confirmation of Lesson Requirements

• [X] is the original work of the author(s), or that any content derived from another source is reused with permission and appropriate attribution
• [X] aligns with The Carpentries Code of Conduct
• [X] is published under a CC-BY or CC0 license
• [X] uses The Carpentries lesson template or Carpentries Workbench without significant customisation/adaptation.

JOSE Submission Requirements

• [ ] the lesson repository includes paper.md and paper.bib files as described in the JOSE submission guide for learning modules

Potential Reviewers

No response

[tobyhodges]

Thank you for submitting this lesson for review, @gperu.

My capacity for managing lesson reviews is quite limited at the moment and I will not be able to handle reviews of all of your submitted lessons simultaneously. If you have a preference for which lesson(s) you would like us to prioritise for review, please let me know and I will do my best to focus on that/those first.

[astroDimitrios]

Popping this here with my editor checks which I will be updating over the next couple of weeks :)

Editor Checklist - Responsible machine learning in Python

Accessibility

• [ ] All figures are also described in image alternative text or elsewhere in the lesson body.

• [ ] The lesson uses appropriate heading levels:

  • [ ] h2 is used for sections within a page.
  • [ ] no "jumps" are present between heading levels e.g. h2->h4.
  • [ ] no page contains more than one h1 element i.e. none of the source files include first-level headings.

• [ ] The contrast ratio of text in all figures is at least 4.5:1.

• replace this with any further comments relating to the accessibility of the lesson.

Content

• [ ] The lesson teaches data and/or computational skills that could promote efficient, open, and reproducible research.

• [ ] All exercises have solutions.

• [ ] Opportunities for formative assessments are included and distributed throughout the lesson sufficiently to track learner progress. (We aim for at least one formative assessment every 10-15 minutes.)

• [ ] Any data sets used in the lesson are published under a permissive open license i.e. CC0 or equivalent.

• replace this with any further comments relating to the lesson content.

Design

• [ ] Learning objectives are defined for the lesson and every episode.

• [ ] The target audience of the lesson is identified specifically and in sufficient detail.

• replace this with any further comments relating to the design of the lesson.

Repository

The lesson repository includes:

• [ ] a CC-BY or CC0 license.
• [ ] a CODE_OF_CONDUCT.md file that links to The Carpentries Code of Conduct.
• [ ] a list of lesson maintainers.
• [ ] tabs to display Issues and Pull Requests for the project.

• replace this with any further comments relating to the lesson repository.

Structure

• [ ] Estimated times are included in every episode for teaching and completing exercises.
• [ ] Episodes lengths are appropriate for the management of cognitive load throughout the lesson.

Supporting information

The lesson includes:

• [ ] a list of required prior skills and/or knowledge.
• [ ] setup and installation instructions.
• [ ] a glossary of key terms or links out to definitions in an external glossary e.g. Glosario.

General