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
Project for IEEE/ACM TASLP “Bayesian Neural Network Language Modeling for Speech Recognition”
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
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
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
Final project of the Data Visualization course, Ariel university.
This is the code for post-processing the shallow shadow tomography data and doing error mitigation on the noisy hardware.
Simple implementation of knn & Bayesian Learning & Random Forest from Scratch
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