Machine Learning

Machine Learning exercises for my subject of Machine Learning (Aprendizaje Automático) at University of Granada (UGR) of 2015/2016.

This repository contents Machine Learning exercises divided by sections, corresponding to the exercises of the subject. Each folder contents the R code and a R-markdown that you can use on RStudio and see the results of the exercises, edit the content, etc. Also, a PDF is included (in Spanish) with the results and conclusions of the differents exercises.

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1. Perceptron and linear regression

Here, I implemented a linear perceptron, pocket perceptron and linear regression, and they are tested with random generated data to see how perceptrons and regressions works.

2. Linear methods, overfitting and regularization

Here, I implemented differents linear methods:

• Gradient descent, also known as steepest descent.
• Coordinate descent.
• Newton method.
• Logistic regression.

Also, there are implementations to measure the overfitting of the model, apply regularization to a model, weight decay, etc and model selection according to their perfomance.

3. Generalized linear models, SVM, KNN and Trees

In this section, I followed the book An Introduction to Statistical Learning with Applications in R (ISLR), and do different exercises with generalized linear models, Support Vector Machine, KNN, Boosting, Bagging, Random Forest and Trees... and different dataset.

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Some data visualization are made with ggplot2, so, in order to execute the code, this package is required.