ML+X — A machine learning community – data science @ uw

ML+X is a community of practice that brings together students, researchers, and industry professionals who share an interest in using machine learning (ML) methods (e.g., regression, classification, clustering, NLP, reinforcement learning, etc.) to advance their work (X). Community events and activities aim to help ML practitioners explore the challenges and pitfalls of ML, share knowledge and resources, and support each others’ work. Join the community to learn more!

ML+X Forum

The ML+X forum features short talks and group discussions on real-world applications of machine learning (ML)

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ML+Coffee Social

Discuss and work on machine learning (ML) projects with other practitioners on campus at the community's monthly social!

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Submit an Event

Contribute to the community by running your own events! Reading groups, workshops, and more!

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Join the Community!

Anyone who is working with or wants to learn more about ML methods is welcome to join! Join our Google group to be notified of upcoming events, and join our Slack channel (#ml-community) to stay connected with the community! New members are encouraged to introduce themselves in Slack.

Join Google Group Join Slack Group

If you have any trouble joining either platform, please send an email to facilitator@datascience.wisc.edu.

Interested in Sponsoring Us?

We are actively seeking sponsorships as we continue to grow and expand of our thriving community! Your generous support would play a pivotal role in enabling us to consistently deliver unparalleled value to our members through machine learning workshops, discussion forums, social gatherings, and more. Visit the sponsorship page to learn how your organization will be represented as a sponsor of ML+X.

Events

Each monthly ML+X forum highlights two ML applications that share a theme followed by communal discussions and project feedback.

Where: Orchard View Room (rm. 3280), Discovery Building & via Zoom. Join the google group to receive a calendar invite (with Zoom link) and other updates.
When: Monthly on Tuesdays, 12-1pm. Fall 2023 dates include Sept. 19th, Oct. 10th, Nov. 7th, and Dec. 12th. Spring 2024 dates include Feb. 13th, March 12th, April 9th, and May 14th.
Share Your Work! We encourage anyone who is using ML in their work to present at one of the ML+X forums. If interested, please fill out this brief google form.

Spotlight Events	Event Archive
Artificial evolution of a fluorescent protein technology using large language models Mike Killoran \| Large Language Models for Protein Engineering \| 5/2/2023 Characterizing the building blocks of human perceptual similarity in abstract drawings using deep neural networks Kushin Mukherjee \| Representations of Learning \| 4/11/2023 Bias in machine learning: the good, the bad, and the ugly Chris Endemann \| Bias & Fairness \| 12/6/2022 Computer vision for animal monitoring: challenges of a real-world application Joao Dorea \| Computer Vision \| 10/4/2022	Each forum highlights a different machine learning topic for community members to explore and discuss. Past themes have included methods for evaluating bias and fairness, large language model applications, computer vision applications, representations of and for learning, computing resources, and more. Click the button below to explore all past topics and projects discussed at the monthly ML+X forum! Access Archive

Spotlight Events

Event Archive

Artificial evolution of a fluorescent protein technology using large language models

Mike Killoran | Large Language Models for Protein Engineering | 5/2/2023

Characterizing the building blocks of human perceptual similarity in abstract drawings using deep neural networks

Kushin Mukherjee | Representations of Learning | 4/11/2023

Bias in machine learning: the good, the bad, and the ugly

Chris Endemann | Bias & Fairness | 12/6/2022

Computer vision for animal monitoring: challenges of a real-world application

Joao Dorea | Computer Vision | 10/4/2022

Each forum highlights a different machine learning topic for community members to explore and discuss. Past themes have included methods for evaluating bias and fairness, large language model applications, computer vision applications, representations of and for learning, computing resources, and more. Click the button below to explore all past topics and projects discussed at the monthly ML+X forum!

Access Archive

Anyone interested in ML is welcome to attend the community’s monthly social, “ML+Coffee”. Coffee and tea provided! Attendees are encouraged to bring their laptops to discuss any ML problems/projects.

Where: Room 1145 of the Discovery Building. This room is located on the west side of the building on the first floor — near to the building exit that leads off to Subway/QQs/Library.
When: Monthly on Wednesdays, 9-11 AM (the morning after each ML+X forum). Show up anytime between 9-11 AM and stay for as long you’d like. Fall socials will take place on Sept. 20th, Oct. 11th, Nov. 8th, and Dec. 13th.

ML+X collaborates closely with UW-Madison’s Carpentries Community to develop and offer short (1-3 half days) ML-related workshops throughout the year including:

Intro to ML with Scikit Learn (Fall)
Intro to Deep Learning with Keras (Spring)
Intro to High Dimensional Data Analysis (Summer)

Registration required! Subscribe to the Data Science @ UW Newsletter to be notified when registration opens.

Have a paper you want to discuss? Have a demo you want to give? Want to run a lunch social or a mini workshop?

Then reach out and we can help facilitate, advertise, or host your event within the ML+X community!

Send an email to facilitator@datascience.wisc.edu to discuss more!

Workshops

Schedule

Please see below for a list of short (1-3 day) machine learning workshops offered by UW-Madison’s Carpentries Community. Upcoming workshops and registration are announced in the Data Science @ UW Newsletter on a fortnightly basis.

Intro to ML with Scikit Learn (Fall)
Intro to Deep Learning with Keras (Spring)
Intro to High Dimensional Data Analysis (TBA)
Building Transformer-Based Natural Language Processing Applications (TBA)

Learn to Teach & Teach to Learn

Our ability to offer numerous workshops year-round is made possible through UW-Madison’s Carpentries Community — a group of volunteers dedicated to teaching computational workshops that quickly advance researchers’ ability to reproducibly analyze, visualize, and model data.

If you’re curious about helping or instructing at a workshop, review the FAQs page and join the Carpentries Google group to be notified of upcoming volunteer opportunities. You do not need to be an expert in a topic that you’d like to help teach, and members have plentiful opportunities to attend workshops to advance their skills. Members also have the opportunity to participate in an optional instructor training program.

Thank You ML+X Sponsors

ML+X is grateful to our sponsors for providing us monetary support in achieving our goals helping ML practitioners explore the challenges and pitfalls of ML, sharing knowledge and resources, and supporting each others’ work.

See and learn about our sponsors by clicking their logos below.

Participant Level

Leadership Team

Interested in Joining ML Community Leadership Team?

The ML Community welcomes members to join its leadership team! The leadership team helps grow and sustain a lively and engaged community of practice, and ensures ML practitioners across campus have ample opportunities to discuss challenges, learn from one another, and support each other. Anyone passionate about ML and community is welcome to join—including students, no minimum experience required!

The leadership team meets once per month to: brainstorm and plan events and resources to serve the needs of ML practitioners on campus; select themes and recruit speakers for events; strategize ways to create a lively community and increase engagement; collaborate with other ML-related groups on campus.

To join the leadership team, please email endemann@wisc.edu with a brief summary of your interest in machine learning and/or communities of practice.

Salsabil Arabi