This is a concise introduction to probabilistic modeling and machine learning that shows readers how to design models for real data, how to compute with them, and how to evaluate and revise them. The book begins with conjugate models and regression, then moves through mixture models, topic models, matrix factorization, exponential families, hierarchical models, and deep probabilistic models. In parallel, it develops the main computational tools: MAP estimation with stochastic optimization, Gibbs sampling, coordinate-ascent variational inference, stochastic variational inference, and black box variational inference. The result is a coherent treatment bridging Bayesian statistics, probabilistic modeling, machine learning, and connecting to modern artificial intelligence through deep generative models, diffusion models, and large language models. It is an ideal resource for graduate students, practitioners, and researchers.

This is a concise introduction to probabilistic modeling and machine learning that shows readers how to design models for real data, how to compute with them, and how to evaluate and revise them. The book begins with conjugate models and regression, then moves through mixture models, topic models, matrix factorization, exponential families, hierarchical models, and deep probabilistic models. In parallel, it develops the main computational tools: MAP estimation with stochastic optimization, Gibbs sampling, coordinate-ascent variational inference, stochastic variational inference, and black box variational inference. The result is a coherent treatment bridging Bayesian statistics, probabilistic modeling, machine learning, and connecting to modern artificial intelligence through deep generative models, diffusion models, and large language models. It is an ideal resource for graduate students, practitioners, and researchers.

This is a concise introduction to probabilistic modeling and machine learning that shows readers how to design models for real data, how to compute with them, and how to evaluate and revise them. The book begins with conjugate models and regression, then moves through mixture models, topic models, matrix factorization, exponential families, hierarchical models, and deep probabilistic models. In parallel, it develops the main computational tools: MAP estimation with stochastic optimization, Gibbs sampling, coordinate-ascent variational inference, stochastic variational inference, and black box variational inference. The result is a coherent treatment bridging Bayesian statistics, probabilistic modeling, machine learning, and connecting to modern artificial intelligence through deep generative models, diffusion models, and large language models. It is an ideal resource for graduate students, practitioners, and researchers.