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.