Artificial Intelligence: A Modern Approach - Stuart Russell and Peter Norvig

This book is widely regarded as the standard textbook on artificial intelligence. It provides a comprehensive introduction to the field, covering topics such as search algorithms, game playing, knowledge representation, machine learning, and natural language processing.

Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book provides a detailed introduction to deep learning, which is a subfield of machine learning that uses neural networks to learn from data. It covers topics such as convolutional neural networks, recurrent neural networks, and generative models.

Python Machine Learning - Sebastian Raschka and Vahid Mirjalili

This book introduces machine learning using the Python programming language. It covers topics such as classification, regression, clustering, and deep learning, and includes practical examples and code samples.

Natural Language Processing with Python - Steven Bird, Ewan Klein, and Edward Loper

This book provides an introduction to natural language processing, which is a subfield of AI that focuses on analyzing and understanding human language. It covers topics such as tokenization, part-of-speech tagging, parsing, and sentiment analysis.

Robotics: Modelling, Planning and Control - Bruno Siciliano and Lorenzo Sciavicco

This book provides an introduction to robotics, which is a field of AI that focuses on designing and building intelligent machines that can interact with their environment. It covers topics such as kinematics, dynamics, trajectory planning, and control.

These books provide a broad introduction to the AI knowledge body, and can serve as a starting point for further learning and exploration.

There are many excellent books on data structures, and the "best" one will depend on your level of knowledge, learning style, and specific interests. However, here are some popular and highly recommended data structure books:

Introduction to Algorithms - Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

This is a classic textbook that provides a comprehensive introduction to algorithms and data structures. It covers topics such as sorting and searching, graphs, dynamic programming, and more. It is widely used in computer science courses around the world and is considered an essential reference for anyone working in algorithms and data structures.

Data Structures and Algorithms in Python - Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser

This book provides an introduction to data structures and algorithms using the Python programming language. It covers topics such as arrays, linked lists, stacks, queues, trees, and graphs, with a focus on implementation and analysis.

Algorithms - Robert Sedgewick and Kevin Wayne

This book provides a comprehensive introduction to algorithms and data structures, with a focus on practical applications. It covers topics such as sorting and searching, graph algorithms, string processing, and more. It includes Java code examples and is suitable for both beginners and advanced learners.

The Algorithm Design Manual - Steven S. Skiena

This book provides a practical guide to designing and analyzing algorithms and data structures. It covers topics such as sorting and searching, graph algorithms, string processing, and more, with a focus on real-world applications. It includes numerous examples and exercises, making it ideal for self-study or classroom use.

Data Structures and Algorithms Made Easy - Narasimha Karumanchi

This book provides a simplified introduction to data structures and algorithms, with a focus on practical implementation. It covers topics such as arrays, linked lists, stacks, queues, trees, graphs, and sorting algorithms, and includes numerous examples and practice problems.

These are just a few examples of the many excellent data structure books available. It's important to choose a book that aligns with your learning goals and style, and that provides clear explanations and examples to help you master this critical area of computer science.