In this series of workshops, we learned about how to use a multilayer perceptron (MLP) to classify objects and do regression. Students then had the chance to form teams and work on their own project over the next week.
In the world of supervised machine learning, the goal is almost always to find a function that matches some data with input and output features. The simplest example is linear regression, which might take humidity as an input and expected rainfall as an output. MLPs and other kinds of neural networks just take this idea one step (or several) further, allowing you to model complex relationships with your data.
Try it out
See our slideshow and recorded presentation from Jan 23, 2023.
See Our Creations
If you're interested to learn and chat about what members worked on, please join the MSU AI Club Discord server ! We share learning resources and additional material from the workshop.