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