Intro to Unsupervised Learning: 2021

GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials

A Public Repository containing all of NYP AI’s past event materials - GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials

GitHubNYP-AI

NYP AI hosted its first-ever Unsupervised Learning event on 8th May 2021. Armed with detailed slides and concise code, we were ready to introduce a new Machine Learning paradigm to our members, through two of its more popular applications: Clustering and Dimensionality Reduction

To start off, we cleared all misconceptions regarding Supervised vs Unsupervised learning. This involved providing side-by-side comparisons of their definitions, algorithms and applications.

They look very similar, don't they....

Ever wondered what "Dimensionality" meant? We got that covered too...

Clustering: K-Means

Moving on to our first Algorithm of the day: Clustering with K-Means. With clear diagrams to aid our members' understanding, we were able to get the point across quickly.

Cen...cen what?

Unsure of how many clusters to initialize? We got that covered too :)

Dimensionality Reduction: PCA

Moving on to a slightly more difficult concept - Dimensionality Reduction with PCA. Notwithstanding the tough visualizations required to understand, we were able to paint a clear picture using simple diagrams.

Easy visualizations...right?

What better way to understand something than to apply it 🤷.... To end off our theory sessions, we implemented these exact algorithms in code. Having physically implemented these algorithms, our participants were now confident in using them for their own datasets.

Code for K-Means Clustering

Code for PCA Dimensionality Reduction

Till then ~

Not as hard as it seemed, eh? Hoped everyone managed to learn something from today's session. Until then ~ 😊