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.
