This repository contains the exercises done in the course Machine Learning Programming, covering the basics in the machine learning algorithms. The main purpose of this repository is to refresh my memory on the math behind the basic algorithms I use on a daily basis, reminding me how? and why?
It all has been done in MATLAB (Trust me, not my choice).
This repository will contain the exercises, as well as the data used in the labs (if possible).
Each lab contains hidden functions, that are used to evaluate the implementation. I don't think even the TAs could unlock them haha. Anyway, they are needed to run the scripts and make sure you have the right implementation and the expected result.
To be able to run the code, you simply need to use MATLAB & download the data used in the lab if applicable.
Each lab has a html file explaining the lab, explaining the steps, as well as the functions to be implemented (eg. PCA's html explanation).
The functions to be implemented are in the functions subdirectory in each lab's folder (eg. PCA's functions), while my implemented functions are in the implemented functions subdirectory (eg. PCA's implemented functions).
To run the code and make sure everything has been correctly implemented, you would need to run the tp files in each lab's folder, which is generally divided into several files (eg. PCA's lab part 1, PCA's lab part 2, PCA's lab part 3).
The rest of the repository (aka. the plot functions, evaluation functions & data subdirectory) should not be modified, the whole thing is just the functions subdirectory.