These relatively simple MATLAB files were developed in 2019 for the Pattern Recognition class of my studies in Electrical & Computer Engineering in University of Patras.
The main theme of each lab excercise is presented for reference as follows:
| Lab Number | Lab Theme |
|---|---|
| Lab 1 | Intro to MATLAB, matrix calculations, plotting datasets |
| Lab 2 | Classification using class centroids, classification errors |
| Lab 3 | K-means classification |
| Lab 4 | Linear Classification using the Perceptron algorithm |
| Lab 5 | Linear Classification using the Ho-Kashyap algorithm |
| Lab 6 | Multi-level Perceptron Neural Networks and Back-propagation |
These MATLAB files were developed in 2020 for the Data Processing & Machine Learning Projects class of my studies in Electrical & Computer Engineering in University of Patras.
The central theme of each project is summarised in the table below:
| Project Number | Theme/Curriculum covered |
|---|---|
| Project 1 | Naive Bayes Classifier, Likelihood ratio estimation using neural networks (MATLAB implementation of this paper) |
| Project 2 | Support Vector Machines, Hilbert Spaces, Kernel Functions |
| Project 3 | K-means, artificial dimensionality increase, Expectation Maximization in Gaussian Mixture Models |
In addition to the developed code, the project description, as well as my final report on it can be found in each of the project's folders. Written in modern greek.