Books like Essentials of artificial intelligence by Matthew L. Ginsberg

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

An accessible guide to Natural Language Processing, teaching Python programming for text analysis and language technology applications such as predictive text, email filtering, summarization, and translation.

"In this authoritative and accessible one-stop introduction to artificial intelligence, a full first course in AI is presented, making it ideal for a modular undergraduate degree scheme or Masters conversion course." "The book has been designed to provide an understanding of the foundations of artificial intelligence. It examines the central computational techniques embraced by AI - knowledge representation, search, reasoning and learning - and the principal application domains - experts systems, natural language, vision, robotics, software agents and cognitive modelling. In a concluding section some of the major philosophical and ethical issues of AI are also introduced."--BOOK JACKET.

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

Artificial Intelligence (AI) has always been computer science's 'department of clever tricks'. It is concerned with leading-edge problems which are hard for computers even if - like speech and vision - they are easy for people. This book is a practical, do-it-yourself introduction and guide for the personal computer user and student of AI who wants to learn and profit from AI techniques. All the programs are in BBC BASIC. *The Authors* Richard Forsyth was a Senior Lecturer in Computing at the Polytechnic of North London until 1984. He now runs his own business, Warm Boot Limited, which specialises in machine-intelligence applications. His recent books include The BBC BASIC Idea and Expert Systems: Principles and Case Studies, both published by Chapman and Hall. Chris Naylor is currently a full time author, researcher and freelance journalist. His recent books include Build Your Own Expert System and Programs That Write Programs: Choosing and using program generators. He is also a regular contributor on artificial intelligence and allied topics to a wide range of publications, including Practical Computing and The Times. Also available in an Applesoft BASIC version.

This volume contains the elaborated and harmonized versions of seven lectures given at the first Advanced Course in Artificial Intelligence, held in Vignieu, France, in July 1985. Most of them were written in tutorial form; the book thus provides an extremely valuable guide to the fundamental aspects of AI. In the first part, Delgrande and Mylopoulos discuss the concept of knowledge and its representation. The second part is devoted to the processing of knowledge. The contribution by Huet shows that both computation and inference or deduction are just different aspects of the same phenomenon. The chapter written by Stickel gives a thorough and knowledgeable introduction to the most important aspects of deduction by some form of resolution. The kind of reasoning that is involved in inductive inference problem solving (or programming) from examples, and in learning, is covered by Biermann. The tutorial by Bibel covers the more important forms of knowledge processing that might play a significant role in common sense reasoning. The third part of the book focuses on logic programming and functional programming. Jorrand presents the language FP2, where term rewriting forms the basis for the semantics of both functional and parallel programming. In the last chapter, Shapiro gives an overview of the current state of concurrent PROLOG.

A research guide exploring AI principles and practical applications, covering blackboard systems, learning algorithms, neural networks, pattern recognition, signal processing, and real‑world systems from speech recognition to fault diagnosis.