This volume serves as a natural practical extension of Mathematical Methods for Artificial Intelligence, offering a systematic collection of exercises and solutions that guide the reader through the transition from theory to practice. Through a rigorous and progressive approach, the book leads readers to a deep understanding of the mathematical foundations of artificial intelligence: linear algebra, probability, optimization, machine learning, deep learning, and advanced models. Each chapter features structured exercises followed by detailed solutions, designed not only to test knowledge but also to develop reasoning, analytical, and problem-solving skills. More than just a collection of exercises, this text serves as a comprehensive tool capable of transforming abstract concepts into practical skills. The reader is guided step by step through the formalization of problems, the construction of solutions, and the understanding of the principles governing learning models. Aimed at university students, researchers, and professionals, this volume is ideal for those who wish to build a solid and applicable mathematical foundation in the field of artificial intelligence. In a constantly evolving context, the ability to understand and apply the mathematical methods underlying AI represents a fundamental advantage. This book provides the tools to acquire this expertise, making the reader an active participant in their own learning journey.
