Probabilistic Machine Learning - An Introduction

Key Facts and Insights

1. Probabilistic Modelling: The book offers a comprehensive introduction to probabilistic machine learning, a method of building statistical models that provide probabilities for outcomes.
2. Bayesian Methods: Murphy delves into Bayesian methods, elucidating how they allow for incorporating prior knowledge into models and updating these models as new data are gathered.
3. Graphical Models: There is an extensive exposition on graphical models, including both directed and undirected models, which provide a visual and mathematical way to depict complex probabilistic relationships.
4. Mixture Models and EM Algorithm: The book covers the Expectation-Maximization (EM) algorithm and its role in fitting mixture...

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