This repository is a collection of notebooks about Bayesian Machine Learning.The following links display some of the notebooks via nbviewer to ensure a proper rendering of formulas. Dependencies are ...

The course will introduce the basic principles and algorithms used in Bayesian machine learning. This will include the Bayesian approach to regression and classification tasks, introduction to the ...

Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces.

Most Machine Learning algorithms use the GLM, the Generalized Linear Model, also known as regression. A regression finds a linear (straight line) to run through a series of dots in an XY plane in the ...

In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition “Bosch Production Line Performance”.

I have created a python code called regression_algorithms.ipynb for understanding how we are able to implement different approaches of non-linear regression algorithms in machine learning. Non-linear ...

A 250-year-old mathematical theory could be used to create ‘self-aware’ machine learning systems that understand when they are out of their depth, according to a panel of senior quants. Bayes’ theorem ...

Linear regression in machine learning In the field of machine learning, linear regression can be considered a type of supervised machine learning . In this use of the method, the model learns from ...

Linear models for regression: Linear basis function models, Bayesian linear regression, Bayesian model comparison. Linear models for classification: Probabilistic generative models, ... D. Barber, ...