M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

A collection of Methods and Models for various architectures of Artificial Neural Networks

Comparison of Several Bayesian Regression Techniques and Gaussian Processes

Walmart sales prediction with Linear Regression and Random Forests + Bayesian Structure Learning

Application of OPAL (Occam Plausibility Algorithm) based Bayesian learning to SEIRD model of COVID-19 disease spread in Texas

Exercises for the "Data Analytics" course, University of Bologna (2021/2022)

Learning parameters for a bayesian network based on health records data -Assignment 4 (Artificial Intelligence: COL333)

Repository that contains the projects of the Probabilistic Artificial Intelligence class offered in Fall 2021 at ETH Zurich

Implementation of FOD-learn (fully observed data), Expectation Maximisation(Partially Observed Data) and Latent Variable Learning in Bayesian Network

A fork of a Bayesian learning and inference for state space models library, created due to some dependencies mismatching and for posterity because it's used in a private Colab notebook.

Colorectal cancer risk mapping through Bayesian Networks

My Programming Assignments from the CPE 695 Applied Machine Learning Course from Stevens Institute of Technology

Final project of the Data Visualization course, Ariel university.

Visualizing Bayesian Learning

Final project for Bayesian learning & Montecarlo simulation course attended at Polimi in 2022. The objective is to build a Bayesian model able to predict the quality of a given wine

This is the code for post-processing the shallow shadow tomography data and doing error mitigation on the noisy hardware.

Executed a research project on spatial clustering and advanced Bayesian learning and inference for Neyman-Scott processes

This repository contains golang code of Bayesian Learning on a diabetes dataset

Simple implementation of knn & Bayesian Learning & Random Forest from Scratch

Making use of bayesian learning, three common applications are developed.