Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

FAST Change Point Detection in R

Machine Learning in R

As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.

Gradient descent for estimating inbreeding spatial coefficients from pairwise-IBD estimates

Linear Regression, Classification, Naive Bayes Spam Classification

Flexible deep learning neural network. Implements a multi layer perceptron and autoencoders.

In this example I show how Extreme-Gradient-Boosting can be used in a Survival Analysis database. At the end the importance of the variables is shown.

Reports of the assignments: Decision Models a.y. 2018/2019

A logistic regression model to classify the number of comments that a blog receives based on 278 features. Data-set from UCI Irvine

Linear-Regression-using-Gradient-Descent

This project goes over data science theories and data preprocessing, leveraging the Titanic data set provided by Kaggle. The goal is to determine which passengers will likely survive or perish the monumental tragedy. The binary classification problem is addressed using two methods, each with three machine learning algorithms. The first approach …

CAP5615 Intro to Neural Networks class at FAU, Summer 2018