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# System prepended metadata

title: Teaching ML 2021 Workshop

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# Teaching ML 2021 Workshop 

## Agenda 

https://teaching-ml.github.io/2021/#schedule-for-workshop-day-september-13-2021



## Community Connect

Description: During the community connect, participants will have the opportunity to discuss the workshop’s papers with the authors. This is a chance to connect over emerging pedagogical practices and philosophies while also building a more robust community of machine learning educators.

TLDR: This is like a poster session, but without posters. It's a chance for conversation with the authors! 

* List of [accepted papers](https://teaching-ml.github.io/2021/#accepted-papers)


## Workshop Discussions

Top voted topics (in order of preference/votes)
* ML and Pedagogy, Inquiry-based learning
    * Sarah Brown
    * Katherine Kinnaird (until 20 past)
    * Nora McGregor
    * Hilde Weerts
    * Erik Marx
* Textbooks for teaching ML
    * Patrick Glauner
    * 
* Teaching ML without pre-requisites
    * Nora McGregor (2nd choice)
    * Peter Steinbach (2nd choice)
    * Viviana Acquaviva (2nd)
    * 
* Teaching ML at the undergraduate level
    * Annajiat Alim Rasel
    * 
* Teaching ML in STEM (Beyond CS/Math)
    * Peter Steinbach
    * Cornelia Gamst
    * Viviana Acquaviva
    * 
* Teaching ML and the Internet of things
    * Add name here if interested in this topic
    * 
* Ungrading and/or standards based grading methods
    * Add name here if interested in this topic
    * Sarah Brown (2nd choice)
* (Other ideas?)


### Room notes/tasks

Below are list of questions you may want to consider
* What are your top takeaways from your conversation?
* What are questions/concerns that you have about teaching?
* What is one thing from this conversation you want to try in your classroom?

We would love you summarize your discussion to the workshop starting 5pm CEST.

### Individual room notes (if needed) 

Feel free to create your own pad on hackmd.io (github or google accounts required, service is free of charge). You can also use this pad for notes, if you like.

#### ML and Pedagogy, Inquiry-based learning

- eg in postgrad cert for librarians
    - set out somethign they would like to achieve then build toward that
    - https://www.bl.uk/case-studies/computing-for-cultural-heritage-student-projects
- constrained, set them up to ask the next question
    - eg at the bottom of a lab on kmeans, what happens if some of the points already have labels to get them to think about kmeans
- in a more theory elementary course, final is to teach elements from a paper of a big ml course
    - they maintain an engagement journal over the course of the semester
    - build up toward reading a paper
    - eg list 3, mark as interesting or not
    - choose one paper, talk about ethical aspect
    - resource, [paper on reading papers](https://github.com/ml4sts/handbook/blob/main/resources/keshav_howtoread.pdf)
- sarah idea: propse reflective questions on assignemnts
- sarah motivation for trying to do this: [bandwidth recovery](https://styluspub.presswarehouse.com/browse/book/9781620366059/Bandwidth-Recovery)
- challenge or problem based learning
- how to balance scaffoling with inquiry
    - define learning goals at different levels of bloom's taxonomy and have them progress throughthat
    - more structure first, then move into open ended project
    - needs more scaffolding on the project planning
- idea: ground topics in a theme to have them get data that that's of interest
    - group students to learn similar topics
    - curate lists of datasets
    - require datasets with good metadata
    - think about datasheets for datasets


#### Textbooks for teaching ML

#### Teaching ML without pre-requisites
    
#### Teaching ML at the undergraduate level

#### Teaching ML in STEM (Beyond CS/Math)

https://hackmd.io/G0CgoDA-QPSOllh1QcIq8Q?edit

Below are list of questions you may want to consider
* What are your top takeaways from your conversation?
    * do exciting realistic examples early on
    * you are not alone ("never teach alone")
    * easy problems are not always easy
* What are questions/concerns that you have about teaching?
    * realistic scalability (for an individual instructor) -> individual feedback to learners
    
* What is one thing from this conversation you want to try in your classroom?
    * automatic grading
    * varying k in kNN classification to explain limits 

#### Teaching ML and the Internet of things

#### Ungrading and/or standards based grading methods
    
## Q & A

- how to define good examples (for exposing learners early)?
    - at best, use examples which are relevant for the field (pros & cons in comparison to standard methods)
    - build up confidence with learners
    - share ML news that are relevant to the class
    - first 5 minutes in class, share sneak insight into current ML news
    - channels: (only scan titles of stories and move that into bookmarks)
        - twitter
        - reddit: subreddit for ML
        - arxiv moderation
        - newsletters: the batch, 
    - example from GLAM:
        - In our Intro to AI for Glam course we have a really quick quiz around designed to get folks thinking about when they would use machine learning or not....https://carpentries-incubator.github.io/machine-learning-librarians-archivists/02-AI-ML-concepts/index.html 
        - Example: a) Counting the number of people in a museum using information from entry and exit barriers. b) A search system that looks for images similar to a user submitted sketch. c) A system that recommends library books based on what other users have ordered. 
    