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
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
Implementation of FOD-learn (fully observed data), Expectation Maximisation(Partially Observed Data) and Latent Variable Learning in Bayesian Network
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
This is the code for post-processing the shallow shadow tomography data and doing error mitigation on the noisy hardware.
Final project of the Data Visualization course, Ariel university.
Simple implementation of knn & Bayesian Learning & Random Forest from Scratch
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