Master the mathematics that powers modern machine learning, artificial intelligence, data analytics, and large language models.
Information theory is the hidden language of data science. Every time a model minimizes cross-entropy loss, every time features are selected using mutual information, and every time an AI system predicts the next token, information theory is at work.
Information Theory for Data Science provides a practical, modern introduction to the concepts that drive today's data-driven technologies. Starting with the foundations of probability and information, this book builds step-by-step toward entropy, divergence measures, feature selection, machine learning applications, deep learning, generative AI, and large language models.
Unlike traditional information theory texts that focus primarily on communication systems, this book emphasizes real-world applications in data science and artificial intelligence, helping readers connect mathematical concepts directly to modern analytics and machine learning workflows.
Inside You'll Learn:
Self-information and surprisal
Shannon entropy and uncertainty measurement
Joint, conditional, and differential entropy
KL divergence and Jensen-Shannon divergence
Mutual information and dependency analysis
Feature selection using information-theoretic methods
Decision trees and entropy-based learning
Cross-entropy loss in machine learning
Information bottleneck theory
Representation learning and latent information
Information theory in deep learning
Natural language processing and language modeling
Computer vision and image information analysis
Generative AI and probabilistic modeling
Data compression and source coding
Channel capacity and reliable communication
Rényi entropy, Tsallis entropy, and information geometry
Causal information theory
Information theory for Large Language Models (LLMs)
Practical Features
- Clear explanations with intuitive examples
- Mathematical derivations presented step-by-step
- Python implementations throughout the book
- Real-world machine learning case studies
- Visual diagrams and illustrations
- End-of-chapter exercises
- Five complete data science projects
- Comprehensive formula reference
- Interview questions and solutions manual
Whether you are a data scientist, machine learning engineer, AI practitioner, computer science student, researcher, or quantitative analyst, this book will help you develop a deep understanding of how information flows through modern intelligent systems-and how to use that knowledge to build better models and make better decisions.
From entropy to machine learning, AI, and modern analytics, discover the mathematical foundation behind the information age.
