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Most common price: £79 (54 days, 100.0%)

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Description

Information Theory for Modern Machine Learning: From Theory to Python Practice bridges the classical foundations of information theory with the algorithms that power modern AI. It presents a unified view of machine learning as the transformation, compression, and allocation of information, connecting core quantities such as entropy, mutual information, KL divergence, and Fisher information to practical methods in supervised learning, representation learning, generative modeling, gradient descent optimization, and deep architectures. Unlike texts that treat information theory as a self-contained topic, this book shows how information-theoretic ideas directly explain why familiar objectives and algorithms work and how they generalize to today’s large-scale models. What you’ll learn How entropy, cross-entropy, KL divergence, and mutual information organize classical ML objectives and generalization behavior How optimization dynamics relate to information geometry and Fisher information How neural networks reshape uncertainty via nonlinear transformations and Jacobians How representation learning can be understood through compression–prediction trade-offs (Information Bottleneck) How modern generative modeling methods shape distributions: VAEs, GANs, normalizing flows, and diffusion models How self-supervised and contrastive learning relate to mutual-information objectives and bounds An information-theoretic perspective on Transformers and attention as adaptive information routing An integrated framework for reinforcement learning, maximum-entropy control, intrinsic motivation, and RLHF How information-theoretic principles support lifelong and continual learning, addressing stability–plasticity trade-offs, representational interference, and knowledge accumulation as prerequisites for artificial general intelligence (AGI) Designed for learning and teaching Clear conceptual narrative with rigorous mathematical development Conceptual, mathematical, and proof-based exercises in every chapter Computational exercises and notebooks in Python to help you experiment, visualize, and internalize the ideas Who this book is for Advanced undergraduates and graduate students in machine learning, statistics, applied math, or engineering Researchers and practitioners who want a principled framework that connects modern ML to first principles Readers with basic background in probability and linear algebra (no prior information theory required) Companion code Python implementations of major machine learning algorithm from scratch are available on GitHub: github.com/TangWPI/book_ml_python

Product Specifications

Format: hardcover
ASIN: B0G8D7BVMW
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 16 December 2025
Listed Since: 18 December 2025

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Information Theory for Modern Machine Learning: From Theory to Python Practice

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