Teaching Machine Learning for Social Good

Summary of WiMLDS-Hyderabad (Hyderabad Women in Machine Learning and Data Science) 10th Online Meetup.

WiMLDS-Hyderabad’s 10th Online Meetup was held on 2nd September 2020. Here is a quick summary of the vital points covered in the presentation. For the detailed presentation, refer to the video below.

Teaching Machine learning for Social Good

Disclaimer: All the images used in this article have been taken from the presentation.

Speakers

  • Amanda Su is the Co-Director of Delta Analytics’ Teaching Fellows program. She is currently a Health Policy PhD student at Stanford University specializing in health economics. Prior to Stanford, Amanda was a data scientist at Nuna Health, where she developed and productionized methods to improve patient-physician matching using machine learning and econometric approaches. Before data science, Amanda worked in economic consulting for three years. Amanda holds a Bachelor degree in Economics from the University of California, Berkeley.
  • Melissa Fabros is the Teaching Lead at Delta Analytics’ Teaching Fellows program. Melissa is a machine learning and full-stack engineer for Kiva.org. She possesses 3 years of production software engineering experience with two open-source technical internships under Google Summer of Code and Rails Girls Summer of Code. Previous to working in engineering, Melissa did her PhD work in English at the University of California, Berkeley and lectured at the University of California, Merced.

Here is a quick recap of the webinar, highlighting some salient points

DATA ANALYTICS

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Amanda talked about Data Analytics mission which is to “increase access and usefulness of data to empower changemakers working to better their communities“. Data Analytics has two core programs:

  • Data Service Grant – Helping non-profits to leverage skilled data professionals on projects.
  • Machine Learning for Good – Empowering people to learn the basics of Machine Learning to build technical capacity and leverage the data that is present around the world.

MACHINE LEARNING CURRICULUM

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Amanda then talked about the Data Analytics’ Machine learning Curriculum which they have open-sourced on Github. Amanda highlights that this curriculum was developed with the goal of being accessible. The slide above shows a beginner-friendly and intuitive introduction to Decision Trees.

TEACHING FELLOWS PROGRAM

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Melissa explained that the goal of Data Analytics is to support educators around the world. With that view in mind, they launched the Teaching Fellows program in 2017. Teaching Fellows are full-time data professionals who volunteer their time for free to build, code, and teach a curriculum that makes machine learning tools and knowledge more accessible to communities around the world.

BUILDING SKILLS & COMMUNITY

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Amanda touched upon the three motivating principles behind launching the Teaching Fellows Program :

  • Delta shouldn’t be a bottleneck
  • Building local capacity around the world, and
  • Participation in the machine learning community should reflect the world we serve.

THEIR JOURNEYS

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Amanda and Melissa then discussed their journeys into ML and AI. Interestingly both come from a non-coding background and took not-so-typical paths to enter into the Data Science field.

We wrapped up the webinar with a Q&A session which included questions like how to get into Data Science, advice for people with non-programming background, becoming part of the teaching fellows program etc.

Resources:

Connect with the WiMLDS Hyderabad Community:

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