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hep_ml_lessons's Introduction

Machine Learning for Particle Physics

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This repository generates the corresponding lesson website from The Carpentries repertoire of lessons.

Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of [issues][FIXME] for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag good_first_issue. This indicates that the maintainers will welcome a pull request fixing this issue.

Maintainer(s)

Current maintainers of this lesson are

  • Luke Polson

Authors

A list of contributors to the lesson can be found in AUTHORS

Citation

To cite this lesson, please consult with CITATION

hep_ml_lessons's People

Contributors

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Forkers

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hep_ml_lessons's Issues

Suggested improvements to lesson

Thanks @lukepolson for this amazing work! Below I list some comments. I can start working through some of them if you agree

https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/01-introduction.md#what-is-machine-learning
"Classification. The input is multi-dimensional data points and the output is an integer (which represents different classes). Consider the following example with two classes:”
Couldn’t this be confusing because in the hands-on part we have an example with two classes (and we’re trying to classify) but the output of our machine learning algorithm isn’t an integer.

https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/01-introduction.md#what-role-does-machine-learning-have-in-particle-physics
"(My Research)..."
I think the link to ML could be made clearer here. Is there anyway you can tie it back in to explain whether it’s regression/classification/generation?

https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/01-introduction.md
I’m not sure the key point "In general, machine learning is about designing a function f...."
is totally clear from the lesson. Thinking of the key points as a summary, maybe a more suitable key point could be “The 3 main tasks of Machine Learning are regression, classification and generation”. This would also fit with the 3 sections into which you’ve split this lesson.

https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/02-mltechnical.md#loss-function-and-likelihood
I think the last sentence "Thus minimizing the MSE..." might be a bit of a logical jump. Can you add a sentence in between to help the logic? Maybe you could also connect this statement to the plots more.

A general comment is that it'd be really nice if we could also find links to free, online material as well as the books, to ease accessibility. e.g. https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/02-mltechnical.md#regression-classification-generation

https://github.com/lukepolson/HEP_ML_Lessons/blob/gh-pages/_episodes/03-Resources.md#proficiency-in-python
You mention 3 python libraries but only list 2. Did you forget one? Isn't numpy and pandas enough?

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