MATLAB Machine Learning Projects

This repository contains MATLAB code solutions for two machine learning problems:

Problem 1: k-Nearest Neighbor Method

In this problem, we utilize the k-nearest neighbor method to estimate class conditional density functions and perform classification tasks.

Steps Taken:

1. Estimate class conditional density functions using k-nearest neighbor method.
2. Plot the results using the mesh function in MATLAB.
3. Classify a specific vector (x = [1, -2]^T) based on the density estimation with k = 10.

Problem 2: Data Visualization and Batch Perceptron Method

This problem involves data visualization and applying the Batch Perceptron method for linear discriminant function.

Steps Taken:

1. Plot the 2-D dataset to visualize the samples.
2. Assume a projection function (j) and plot augmented vectors in 3-D using the plot3 function.
3. Use the Batch Perceptron method to find the weight vector in the generalized linear discriminant function.

Files in the Repository:

• problem1.m: MATLAB script for Problem 1.
• problem2.m: MATLAB script for Problem 2.
• README.md: Overview of the project.

Usage:

1. Clone the repository to your local machine using:

git clone 'https://github.com/Prayash-Das/MATLAB-Machine-Learning-Projects.git'

2. Open MATLAB and run the scripts problem1.m and problem2.m to execute the code for each problem.

Notes:

• Ensure MATLAB environment is set up with necessary toolboxes.
• Refer to comments within each MATLAB script for detailed explanations of the code and algorithms used.
• Modify the scripts for experimentation or further analysis as needed.