This repository contains a course project for "Mathematics of Machine Learning" developed in the Julia language. The project consists of four main folders, each focused on a different aspect of machine learning.
This folder contains a demonstration of the least squares method, including the solution to the Point Set Registration problem. The focus of this folder is on the mathematical foundations of machine learning.
This folder contains a practical application of machine learning using the Mines vs Rocks dataset. The goal is to classify the data into two categories using various machine learning models, including SVM, KNN, logistic regression, and others. The models are compared using various metrics to determine the best approach.
This folder contains a theoretical development of stochastic gradient descent and its use in deep neural networks. The focus of this folder is on understanding why the optimization problem solution works in deep neural networks.
This folder contains answers to some of the introductory questions about statistical learning theory found in the book "Learning From Data" by Yaser S. Abu-Mostafa. The focus of this folder is on gaining a deeper understanding of the theory behind machine learning.
To run the code in this repository, you will need to have the Julia programming language installed on your computer. The code has been tested with Julia version 1.5.x and should work with later versions as well.
This project is open to contributions from anyone who is interested. If you have ideas for improvements or corrections, feel free to submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.