PROBABILISTIC MACHINE LEARN-ING FROM SCRATCH Bayesian Methods, Graphical Models & Inference

PROBABILISTIC MACHINE LEARN-ING FROM SCRATCH: Bayesian Methods, Graphical Models & Inference
English | May 16, 2026 | ASIN: B0GZK27XZK | 325 pages | Epub | 8.23 MB
What if machine learning models could explain uncertainty instead of hiding it?Most modern machine learning books teach optimization first: define a loss, compute gradients, and train models. But probabilistic machine learning approaches the problem differently. It asks:What should we believe, and how should those beliefs change when new data arrives?PROBABILISTIC MACHINE LEARNING FROM SCRATCH is a rigorous, implementation-driven guide to Bayesian methods, graphical models, probabilistic inference, and modern uncertainty-aware AI systems. Designed for serious learners, graduate students, ML engineers, and researchers, this book builds the field from first principles with complete derivations and practical code implementations.Inside this book, you will learn:Bayesian probability and statistical inferenceConjugate priors and exponential family distributionsBayesian linear and logistic regressionGaussian processes and kernel methodsDirected and undirected graphical modelsExact inference and belief propagationMarkov Chain Monte Carlo (MCMC)Variational inference and ELBO optimizationHidden Markov Models and latent variable modelsMixture models and the EM algorithmVariational Autoencoders (VAEs)Bayesian neural networksNormalizing flows and diffusion modelsCalibration, uncertainty estimation, and probabilistic decision-makingUnlike many theoretical texts, this book emphasizes implementation and intuition alongside mathematics. Nearly every algorithm is developed step-by-step and implemented using plain NumPy so readers understand not only how to use probabilistic methods, but why they work.This book is ideal for:Machine learning engineersAI researchersData scientistsGraduate studentsAdvanced undergraduate studentsReaders transitioning from classical ML into Bayesian AIIf you want to move beyond black-box models and truly understand uncertainty, inference, and probabilistic reasoning in machine learning, this book provides the mathematical foundation and practical skills to do it.