Machine Learning Basics

This repository contains foundational exercises and implementations for the Machine Learning course (2022/2023). Each notebook focuses on a specific machine learning algorithm or concept, providing hands-on experience with their implementation and application.

Contents

1. Perceptron Algorithm

   • File: perceptron.ipynb
   • Overview: Implementation of the basic perceptron algorithm for binary classification tasks.

2. Perceptron with Delta Rule

   • File: PerceptronWithDeltaRule.ipynb
   • Overview: Enhancement of the perceptron algorithm using the delta rule for weight updates to improve learning performance.

3. Polynomial Ridge Regression

   • File: PolynomialRidgeRegression.ipynb
   • Overview: Application of ridge regression with polynomial features to address multicollinearity and overfitting in regression models.

4. Support Vector Machine (SVM)

   • File: SupportVectorMachine.ipynb
   • Overview: Implementation of SVMs for classification tasks, exploring the concept of maximizing the margin between data classes.

5. Custom Neural Network

   • File: MyNeuralNet.ipynb
   • Overview: Construction and training of a simple neural network from scratch, understanding the forward and backward propagation processes.