£85.93

MIT Press Probabilistic Machine Learning: An Introduction

Navy

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£89.33 £29.33 £42.42 £55.51 £68.60 £81.69 £94.79 08 July 2026 19 July 2026 30 July 2026 10 August 2026 22 August 2026

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Description

Master the foundations of modern artificial intelligence with Probabilistic Machine Learning: An Introduction from MIT Press. This comprehensive text provides a detailed and up-to-date introduction to the field, utilizing the unifying lens of probabilistic modeling and Bayesian decision theory. Designed for learners seeking a deep understanding of how algorithms function, the book builds a solid foundation by covering essential mathematical background, including linear algebra and optimization. It guides readers through basic supervised learning concepts, such as linear and logistic regression and deep neural networks, before moving into complex territory. Whether you are studying deep learning or exploring advanced topics like transfer learning and unsupervised learning, this book serves as a rigorous guide to the mathematical principles driving today's technology. It is an essential resource for anyone looking to understand the probabilistic frameworks that power contemporary computer science and machine learning applications.

Key Features

Comprehensive coverage of probabilistic modeling and Bayesian decision theory to provide a unified view of machine learning.

Includes essential mathematical foundations such as linear algebra and optimization to prepare readers for advanced study.

Explains fundamental supervised learning methods including linear regression, logistic regression, and deep neural networks.

Covers advanced machine learning topics such as transfer learning and unsupervised learning for deeper technical expertise.

Provides up-to-date insights into deep learning methodologies within a probabilistic framework.

Published by MIT Press as part of the Adaptive Computation and Machine Learning series for academic rigor.

Product Specifications

Colour
Navy
Format
hardcover
Domain
Amazon UK
Release Date
01 February 2022
Listed Since
13 May 2021

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No barcode data available

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