Books

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.

Prediction is only the beginning.Modern AI systems must explain their predictions, quantify uncertainty, scale to massive datasets, and operate reliably in real-world environments.How do Support Vector Regression models capture nonlinear patterns?How do Bayesian methods estimate uncertainty?How do Gaussian Processes make predictions with confidence intervals?How can complex AI models remain interpretable and trustworthy?In this advanced second volume, Anshuman Mishra explores the cutting-edge regression techniques that power intelligent prediction systems across machine learning, data science, healthcare, finance, robotics, and scientific research.Discover how modern Artificial Intelligence transforms data into reliable, explainable, and scalable predictions.

Every intelligent prediction begins with a simple question:Can we model the relationship between data and outcomes?Regression is the foundation upon which modern predictive analytics, machine learning systems, and countless AI applications are built.From linear regression and regularization techniques to neural network regression, Gaussian processes, explainable AI, and real-world predictive systems, this book provides a complete roadmap for understanding how machines learn to predict.In Linear and Nonlinear Regression in Artificial Intelligence, Anshuman Mishra combines mathematical rigor, practical implementation, and real-world applications to help readers master one of the most powerful tools in Artificial Intelligence.Discover the mathematics behind prediction—and the science behind intelligent decision-making.

Artificial Intelligence does not learn in certainty.It learns through uncertainty, exploration, randomness, and adaptation.How does AlphaGo evaluate millions of possible moves?How do reinforcement learning agents discover optimal strategies?How do diffusion models generate realistic images?How do autonomous robots navigate uncertain environments?The answer lies in stochastic processes.In this advanced second volume, Anshuman Mishra explores the mathematical foundations behind reinforcement learning, probabilistic deep learning, robotics, generative AI, and emerging stochastic algorithms that are shaping the future of intelligent systems.Discover how randomness becomes learning—and how uncertainty becomes intelligence.

Artificial Intelligence is often described as learning from data.But beneath every learning algorithm lies something even more fundamental:Probability, randomness, and uncertainty.From Hidden Markov Models and stochastic gradient descent to Monte Carlo methods and reinforcement learning, modern AI systems depend on stochastic processes to make predictions, learn from experience, and adapt to changing environments.In Stochastic Processes in Artificial Intelligence, Anshuman Mishra provides a structured and accessible journey through the mathematical foundations that power intelligent systems.Discover how uncertainty becomes intelligence—and why stochastic thinking is essential for the future of Artificial Intelligence.

Every intelligent system faces a fundamental challenge:How do you find the best solution when there are millions—or even trillions—of possibilities?From A* search and heuristic optimization to neural architecture search, hyperparameter tuning, constraint satisfaction, and quantum optimization, modern AI depends on sophisticated strategies for navigating combinatorial search spaces.In this advanced second volume, Anshuman Mishra explores the powerful algorithms that enable intelligent systems to search smarter, optimize faster, and scale beyond brute force computation.Discover how combinatorial thinking drives machine learning, optimization, quantum AI, and the future of intelligent decision-making.The future of AI belongs to those who understand combinatorial complexity.

Artificial Intelligence is not merely about data, learning, or neural networks—it is fundamentally about exploring vast spaces of possibilities.Every search algorithm, planning system, optimization framework, recommendation engine, and machine learning model faces a common challenge: navigating an enormous combinatorial universe of potential solutions.In Combinatorial Thinking in Artificial Intelligence, Anshuman Mishra reveals the mathematical foundations that drive modern AI systems. Through permutations, combinations, graph theory, probability, search strategies, and optimization techniques, readers learn how intelligent systems reason, search, and make decisions in complex environments.If you want to understand why AI algorithms work—not just how to implement them—this book provides the missing mathematical perspective.Discover the combinatorial engine behind intelligence.

Can Hidden Markov Models predict financial market regimes?How are genes discovered from DNA sequences?How do robots determine their location in uncertain environments?Can probabilistic AI still compete with Transformers and Deep Learning?Volume-3 of Hidden Markov Models and AI answers these questions through real-world applications, Python implementations, industrial case studies, advanced projects, and future AI research.Explore how Hidden Markov Models are used in bioinformatics, cybersecurity, finance, robotics, autonomous systems, anomaly detection, and scientific discovery. Learn to build HMM systems from scratch, work with professional AI libraries, and understand the evolving relationship between probabilistic models and deep learning.This volume is designed for readers who want to move beyond theory and develop practical expertise in modern sequential artificial intelligence.

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

What if the unpredictable behavior of AI systems is not a flaw but a consequence of deeper mathematical laws? Explore chaos theory, nonlinear dynamics, fractals, emergence, and complexity to uncover how intelligent systems learn, adapt, self-organize, and evolve in ways that traditional linear models cannot explain.

Explore the advanced world of AI reasoning through SAT and SMT solvers, knowledge representation, intelligent agents, logic programming, formal verification, and neuro-symbolic AI. Learn how modern intelligent systems reason, prove, verify, and explain decisions with mathematical precision.

Discover the mathematical foundations behind intelligent reasoning. Explore propositional logic, predicate logic, theorem proving, SAT and SMT solvers, knowledge representation, formal verification, and AI reasoning systems in one comprehensive guide designed for students, researchers, and AI professionals.

Explore the advanced frontier of Category-Theoretic Artificial Intelligence. Discover how transformers, symbolic reasoning, reinforcement learning, higher categories, topos theory, and compositional learning can be unified through the powerful language of category theory. An essential guide for researchers, AI professionals, and future AGI innovators.

Discover how Category Theory is becoming the mathematical language of modern Artificial Intelligence. Explore categories, functors, natural transformations, compositional learning, neural networks, probabilistic models, and functorial machine learning through an AI-first approach designed for students, researchers, and AI professionals.

Learn Python for Data Science from scratch. A practical 34-page handbook covering NumPy, Pandas, Matplotlib, Seaborn and Scikit-learn with real code examples, solved exercises and a final real-world project.