/supervised

Classification Algorithms for Supervised Learning on popular datasets

Primary LanguagePython

Classification Algorithms for Supervised Learning on popular datasets

This is a repo for some of the HW assignments of the ECEN-689 Machine learning course at Texas A&M University (Spring 2019). Framework, langauge, OS: Keras 2.2.4, scikit-learn 0.20.4, Python 3.7, Windows 8.1

Prerequisites

The "iris-bayes" classification problem uses MATLAB. The remaining algorithms are written in Python and use Keras or scikit-learn. Please install the necessary packages (available as pip install ____).

Description

There are two folders. Each folder contains the problems and the solution (PDF report). The datasets are available on the folders. The files to be run are:

  1. Problem 2 - Bayes, k-NN classifiers on the Iris dataset (iris-bayes): iris.m
  2. Problems 2, 3, 4 - SVM, NN classifiers on the MNIST, fMRI and EEG datasets respectively (svm-nn): MNIST_Rot_Classification.py, fmri-1.py & fmri-2.py, eeg-1.py & eeg-2.py

README last updated with instructions: Jan 2021