Probabilistic machine learning uses probability theory to model uncertainty in data and predictions. Murphy's 'Machine Learning: A Probabilistic Perspective' is a key resource, covering Bayesian inference, graphical models, and latent variable models. This approach is fundamental for understanding many ML algorithms and their mathematical foundations.
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