Part 9. `tidymodels`/Unsupervised Learning/Logistic Regression
Class Video
Slides
Open the slides in a separate window: https://sph-r-programming.netlify.com/slides/09_machine_learning#1
PCA Video (from StatQuest)
KMeans Video (from StatQuest)
Post-Class
Please fill out the following survey and we will discuss the results during the next lecture. All responses will be anonymous.
- Clearest Point: What was the most clear part of the lecture?
- Muddiest Point: What was the most unclear part of the lecture to you?
- Anything Else: Is there something you’d like me to know?
Muddiest Points
The concept of clustering is still a bit muddy for me.
PCA the graph showing contributions from each variable section was muddy for me.
Still struggling to understand what a principal component analysis is
To be honest, I wasn’t really at 100% for this lecture and I wasn’t able to follow what was going on that well, but I plan to review this on my own.
Clustering
still feeling a little confused on PCA. Will be rewatching the videos on the site.
Clustering
Pca
Still trying to wrap my head around PCA/clustering. The homework will help.
A lot of this was muddy. This jumped pretty far ahead of what we’ve learned in our other classes. It’s hard for me to understand the code when I don’t know the math and whatnot behind it.
Points taken. Hope the videos helped.
What we’re doing for our final project/what our options are for models
We will talk about this in class.