Update on our Projects Committee!

The time has come!

October 27, 2021
5 minute read

...but first

The Raiso team first wants to thank Shreya S., Angelina J., Erick M., Isaac S., Vikram, Jiayan L., Daniel Z., Marianne C., Andrew L., Haylie W., and Harita D. for their feedback on our project feedback form! The form was useful for us to get an initial idea of what people would like to see out of a proposed projects committee, and we used it as the core resource for deciding what we should do.

We also would like to apologize for the delay - coming up with a plan for our new projects committee took some trial and error, and some of the plans we wanted to do we ultimately did not have the initial bandwidth for. This includes our attempt to have students form groups and work on the projects that we proposed.

However, we believe the plan that we've come up with not only works the best feasibly but delivers on our core mission. And, it gives our members just as much of an opportunity to learn, with room to be spontaneous and add more things down the road.

The format

Raiso's projects committee for the late fall, winter, and spring quarters will be focused on building well-defined technical projects with a strong student and mentorship community throughout the academic year. The projects will be kaggle projects - if you are unfamiliar, kaggle is an online platform for taking part in and competing in machine learning and data science problems.

What we want to do is remove the "competition" aspect, and lead a group of many students independently, all at the same time, as we build evermore complicated machine learning projects. We'll meet regularly, in person, and guide a workshop with basic deliverables that increase in scope. We'll begin with the basics: setting up a development environment, and analyzing datasets, for example. From there, we'll dive into things such as feature engineering, how to implement and train a model, and how to evaluate the performance of the model.

The goal of the committee is to guide students through ever more complicated machine learning problems in a well-defined setting. We'll hope to integrate a strong mentorship system where students can ask questions to more advanced students if they get stuck on something. Finally, as this is not meant to be "work," but rather an opportunity to learn and have some fun, we'll try and cater most of the events, and even extend into social events down the road!

Next steps

We are deciding on whether or not we will need a synchronous info session to further discuss the logistics of projects, or if we could post a digital, moderated presentation that can be re-watched and where questions can be asked. Either way, we hope to either have this session or post more information by the end of next week.

Until then, we hope everyone has a safe and happy Halloween 🎃! Reach out to us at raiso@u.northwestern.edu with questions.

- The Raiso team