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About Bayesian Machine Learning
This document provides an overview of Bayesian machine learning and probability theory concepts. It discusses supervised and unsupervised learning approaches. It also reviews key probability topics like the sum and product rules, Bayes' rule, and probabilistic inference. Gaussian distributions and their properties are a major focus. Factor graphs are introduced as a tool for automating Bayesian inference using message passing algorithms.
Detailed Information
| Author: | ['John Alamina'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 127 |
| Language: | English |
| Format: | |
| Price: | FREE |
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