The foundation of this repo is projects from COMP-5630 from fall 2024 at Auburn University, as well as any documentation I generated for setting up an intuitive environtment. I also plan on adding an additional ML demos and notebooks that I work on.
Contains all assignments from AI and ML classes taken during the Fall '24 Semester.
Machine Learning with Dr. Farhana.
Contains all projects worked on during this semester, and any adjacent notes + resources I utilized.
Assignment 1 is the first graded assignment we were given for the fall 2024 semester. It essentially served the purpose of pushing us to apply what we have learned so far in the course conceptually in a programatic sense, by manually constructing an implementation of linear regression, logistic regression, and gradient descent. Though nowadays these are almost always implemented through existing packages, by learning how to create these from scratch we are truly attesting to our understanding of the actual math and logic that goes into them rather than mindlessly creating models without knowing why we are choosing to do certain things.
The directory contains the jupyter notebook and any related files we needed, including the pdf instructions and datasets.
Assignment 2 tasked us with applying what we have learned thus far about neural networks, gradient descent using the sigmoid activation, and backpropogation. This assignment had us do both programatic implementations, this time using packages like pytorch or Keras, and sklearn.
The directory contains the jupyter notebook and any related files we needed, including the pdf instructions and datasets.
Assignment 1 involved utilizing our knowledge of some of the search algorithms we have covered including DFS, BFS.
Assignment involving the first implementation of some of the Machine Learning models we covered, including logistic regression, as well as Naive Bayes.
Question 2 dives into Computer vision, and processing an image based on pixel color and estimating a copy of it.
The docs directory simply contains any markdown files with notes and instructions surrounding the environment setup or clarifications for why I chose to setup a notebook in a certain way. This will help for local development if needed, like I chose to do starting with assignment 2. Assignment 1 I originally completed in google colab as that is where is is being graded. We were told if it did not run in colab than it would not be considered.
The notes directory contains any notes surrounding the concepts I studied for the assignment, divided into notes I generated using ChatGPT's GPT-4o model when trying to wrap my head around the concepts, as well as my own notes I may have gathered while studying or building my own understanding.