Opening book details…
Can I read Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) on EtoBox?
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) by Kevin P. Murphy is a science book available to read on EtoBox.
What is Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) about?
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
Who reads Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Kevin P. Murphy
- Publisher
- The MIT Press
- Published
- 2022
- Language
- EN
- ISBN
- 9780262046824
- Category
- science
- Subjects
- Computer Science, Science, Artificial Intelligence (Ai)
- Rating
- 4.4 / 5 (122 ratings)
- Updated
- 2026-03-14
More by Kevin P. Murphy
Browse all works by Kevin P. Murphy
Similar books
- Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) — Carl Edward Rasmusen and Christopher K. I. Williams (2007)
- Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) — edited by Lise Getoor, Ben Taskar (2007)
- Introduction to Machine Learning (Adaptive Computation and Machine Learning series) — Ethem Alpaydin (2010)
- Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) — Daphne Koller and Nir Friedman (2009)