This repository contains all the assignments and the project I did for the Machine Learning course at the School of Applied Mathematics of the Getulio Vargas Foundation (FGV EMAp). The course was taught by Professor Diego Mesquita.
- Nearest Neighbours Method (
$k$ -NN) (10/10) - Linear Regression (8.5/10)
- Logistic Regression and Approximate Bayesian Inference (10/10)
- Selection of Models and Hyperparameters (9.25/10)
- Gaussian Processes for Regression (10/10)
- Neural Networks (10/10)
- Dimensionality Reduction (10/10)
- K-means and Mixture models (8.5/10)
The final project was co-authored by Ana Carolina Erthal, Guilherme de Melo and Bernardo Vargas. The project implements a way of comparing Conformal Prediction with traditional machine learning approaches to generate confidence intervals.