Approximate Inference Methods In Probabilistic Machine Learning And Bayesian Statistics

Approximate Inference Methods in Probabilistic Machine Learning and Bayesian Statistics

Probabilistic Machine Learning

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Understanding Probability

"Understanding Probability" is an essential guide for students, researchers, and professionals to master the principles and diverse applications of probability theory. We meticulously explore core concepts like sample spaces, events, and probability distributions, and delve into advanced areas such as Bayesian inference, stochastic processes, and decision theory. Written for clarity, each chapter provides insightful explanations supported by real-world examples and practical applications. Our book spans multiple disciplines, including statistics, machine learning, finance, engineering, and operations research, making it a valuable resource for readers from various backgrounds. Numerous exercises and problems reinforce learning and equip readers to apply probability theory to real-world scenarios. "Understanding Probability" is an invaluable resource that deepens your understanding of probability and its crucial role in navigating uncertainties in the world around us.