# Teaching

In my teaching, I aim to engage learners in a conversation about the material. I take care to practice the strategies for creating an inclusive computer science learning environment I learned in the [Carpentries instructor Training](http://carpentries.github.io/instructor-training/).   


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:::{grid-item-card} Computer Systems and Programming Tools

This is a new course that I designed to fill gaps identifies by
students and 

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[course site, with syllabi by semester - current](https://compsys-progtools.github.io/)  

[course site - first iterations](https://introcompsys.github.io/)  

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:::{grid-item-card} Machine Learning for Science and Society


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[course site, with syllabi by semester](https://ml4scisoc.github.io/about.html)  

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:::{grid-item-card} Programming for Data Science 

My key revisions to this course have been to:
- more code inspection  
- more authentic tasks in assignments
- participatory live coding instruction  
- mastery based  grading  
- more, smaller assignments

I also [wrote a workshop paper](https://proceedings.mlr.press/v170/brown22a.html) on the design of the course

<!-- Syllabi: 
- [Fall 2023](https://rhodyprog4ds.github.io/BrownFall23/) 
- [Spring 2023](https://rhodyprog4ds.github.io/BrownSpring23) 
- [Fall 2022](https://rhodyprog4ds.github.io/BrownFall22/) 
- [Fall 2021](https://rhodyprog4ds.github.io/BrownFall21/) 
- [Fall 2020](https://rhodyprog4ds.github.io/BrownFall20/) -->
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[course site, with syllabi by semester](https://rhodyprog4ds.github.io/)
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:::{grid-item-card} Data & Society

a required course in the Brown University MS in DS
- [lecture notes(partial)](https://github.com/brownsarahm/data_society_lesson_plans)


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## Short Courses

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:::{grid-item-card} Packaging and Publishing in Python


a [lesson](https://carpentries-incubator.github.io/python-packaging-publishing/) on how to organize, package, and document a python project in the Carpentries Incubator

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:::{grid-item-card} Software Carpentry Workshop at UCSF


I taught the python sections of the two day Software Carpentry workshop hosted by the [UCSF Library March 10-11, 2018](https://gboushey.github.io/2017-03-10-UCSF-Python/).  

I used the [SWC Python Gapminder](http://swcarpentry.github.io/python-novice-gapminder/) curriculum with [minor modifications to emphasize extra features of jupyter notebooks](http://sarahmbrown.org/python-novice-gapminder/).  I used github to manage the student downloads (by .zip files) and the [final post workshop content](https://github.com/brownsarahm/python-novice-gapminder-files/tree/ucsfpostworkshop) and [documented the process](https://github.com/brownsarahm/python-novice-gapminder-files/blob/master/instructor_resources/create_workshop_branch.ipynb) I used to for setup with this jupyter notebook.  

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:::{grid-item-card} Bayesian Nonparametric Guest Lectures

I guest lectured in my PhD adviser Jennifer Dy's graduate machine learning course. I taught two lectures, the first introducing bayesian nonparamerics with the Dirichlet Process/ Chinese Restaurant Process and the second extending to the Indian Buffet Process.

Materials:
 - [Part 1 slides](files/bnp_lecture1_2015.pdf)
 - [Part 1 slides](files/bnp_lecture2_2015.pdf)

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