A large portion of the class grade will be the work you upload to your portfolio. This will be a combination of code, code narrative, reports, and presentations.
In my public repository, ml-portfolio you will find the code and notes I write while learning in Dr. Karen Mazidi’s class Introduction to Machine Learning at the University of Texas at Dallas. While this is currently just a markdown document with links to posted materials, I hope to expand this in the future, perhaps to hold resources accessed by a live site.
gh-pages branch or in /(root)An Overview of ML can be found in the repo! You can read it on here as well
As we are learning R, we need a refresher on C++, so this was that. The documentation explains the importance of what I went over, and the code practice can be found here.
Well this was hard! Work on linear regression can be found here and linear classification can be found here!
Instead of just messing around with logistic regression and naive bayes, we build it from scratch. A blurb about the work can be found here
Group projects are hard! but we got it here Code
We iterate further down the ml rabbit whole with improved classification and regression methods, using some fun math and the art of doing things over and over. The algorithms used are noted here, and implemented in regression, classification, and various ensemble methods.
Just ran through a couple use cases of python for ml, read here
This has been a fun semester of ML! With my resulting understanding of previous ML concepts, its nice to know I understand what improvements my neural networks shown here have over earlier and simpler classification models.