[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

• is the original work of the author(s), or that any content derived from another source is reused with permission and appropriate attribution
• aligns with The Carpentries Code of Conduct
• is published under a CC-BY or CC0 license
• 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 :)
@tompollard

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.

The decision tree image is a bit fuzzy. Is there a clearer version with larger axis labels that could be used?
The attacks bannana image could also be replaced if there is a higher resolution version.

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.

Episodes 5 and onwards don't have any exercises listed on the episode page but they do show time for exercises at the top of the page. Maybe these are in Python and not written up?

There are a few spelling mistakes that need to be fixed.

Design

• Learning objectives are defined for the lesson and every episode.
• The target audience of the lesson is identified specifically and in sufficient detail.

Could you add some more detail to the homepage on the target audience for the lesson - Is this for someone with no background in ML?

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.

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.

Could you add a line about how to unzip a .gz file for the data on Windows/Linux?

General

This has the makings of a great lesson and I think it's a nice idea using the case studies and linking to other papers for further reading.

I'm a bit confused as to where the Python comes in - the setup mentions Python but none of the episodes mention Python at all. Is that because this is the first in a series of ML lessons? Or is there Python somewhere that I'm missing.

There are a few bits missing that say FIXME that need looking at:

• glossary
• discussion
• instructor notes

[astroDimitrios]

Hi @tompollard @gperu I've had a chance to go through the checklist and update the comment above.
Sorry for the delay.
Let me know if you have questions about what I have written and thanks again for submitting this for review!