[ANN] Introduction to Scientific Programming and Machine Learning with Julia MOOC

Dear all, let me announce a new relatively large (15h videos, 15 interactive quizzes with 52 questions, 7 guided exercises), interactive , multi-channel and collaborative course on Introduction to Scientific Programming and Machine Learning with Julia

The course is interactive as thanks to QuizQuestions.jl (thanks @j_verzani !) I managed to move to Documenter the quizzes I wrote in Moodle. All quizzes have a detailed explanation straight after each individual question.
Also, several guided exercises are provided to the students, each with a set of <details>/<index> HTML tags to hide the various passages.
Several widgets enrich the site, for example each page has its own comments thread, thanks to utteranc.es and github flexible issues.

The course is multi-channel because one can watch the videos, clone the course repository and just run the code by itself (the pages are valid Julia files thanks to Literate.jl, or instead consult the “compiled” (“executed” this also doesn’t seem right :slight_smile: ) scripts with the output reported.

SPMLJ is also collaborative as it is github/github CI based and I am very open to share the “platform” to other authors as much the “philosophy” to keep the explanation as simple, intuitive as possible is maintained.
As I say in the video below, I consider it more as a “platform” than as a single one-topic course, and I welcome collaborations to wider its content. Please refer to the relevant section if you wish to contribute.
For example, topics that I think should be important that they are present include symbolic computation, differential equations, statistical models, clustering, reinforcement learning, and so on…

Regarding the content, it currently covers Julia core, the packages Dataframes, Distributions, LsqFit, Plots, Jump for the “Julia” part and Perceptron, Neural Networks and (as a draft) tree-based algorithms for the ML part.

I believe it well complements the courses the like of the MIT Computational Thinking for students from a very different background, e.g. it explains and includes a crash course on git and it goes in very details on some points which are taken for granted in other courses.

This is a video that introduces the course:

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