This repository contains the projects done during the subject "Aprendizaje Automático y Big Data" which I took during my third year studying at UCM.
This folder contains a Jupyter Notebook which we used to show the efficiency difference between scalar and vectorized operations using Numpy.
In this folder, you can find a jupyter notebook in which we try to predict the price of a flat given features like the amount of surface it have, or the number of beedrooms it has using Linear Regression.
This project consists of a jupyter notebook we made to try to predict if a student was going to be admited by a certaing institution given their grade of an exam using Logistic Regression and learned about de effects of L2 regularization.
In this project we learned about Neural Networks and multiclass logistic regression aplying it to the MINIST dataset.
This project consists of an implementation of a Neural Network from scratch using the forward and backward propagation algorithms.
The main objective of this project was to implement regularized linear regression and learn about the effects of overfitting and underfitting.
In this project we learned about tokenizing a text and applied that knowledge to create a Spam filter
This project was the final assignment for the subject. We had to choose a Kaggle dataset and use Neural Networks, Logistic Regression and SVMs to classify it. We chose a dataset consisting of data taken from sensors plugged into a person while doing 4 different movements (rock, paper, scisors, okay) and our goal was to guess which move the person made given the values taken from the sensors.