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Engenharia Médica Aplicada

This repository contains all the machine learning algorithms studied in discipline "Engenharia Médica Aplicada" of Biomedical Engineering course at UNIFESP in the second semester of 2018. All the algorithms were writen in both MatLab and Python Languages. The programatric content of the discipline can be found here.

Getting Started

Prerequisites

To run the algorithms in this repo, you'll need to have either MatLab (or Octave) or Python 3 or both installed.

Python dependencies

To run the python scripts, you'll need to import all of the libraries below:

$ pip3 install numpy

$ pip3 install matplotlib

$ pip3 install spectrum

Algorithms

The algorithms studied in this discipline are divided in the folowing groups:

Feature extraction and selection algorithms:

  • Scalar selection
  • Vectorial selection
  • Receiver Operating Characteristics (ROC)
  • FDR criteria

Pre processing data:

  • Data normalization
  • Outliers removal

Dimensionality reduction and Whitening data:

  • PCA
  • SVD
  • ICA

Classification algorithms:

  • Bayesian
  • Perceptron
  • Perceptron Pocket
  • Euclidean minimum distance
  • Mahalanobis minimum distance
  • LS
  • FDA
  • SVM

Built With

  • MatLab: A software for numerical computation.

  • Gnu Octave: Scientific Programming Language.