The book provides a structured introduction to core areas of artificial intelligence, including machine learning, deep learning, large language models, and agent-based systems. It combines theoretical foundations with practical implementation, using tools such as scikit-learn, Keras, and Ollama. Each chapter includes code examples, exercises, and links to interactive notebooks. The focus lies on conveying applicable knowledge for real-world use. The book is intended for students and practitioners in computer science or related fields and can be used for teaching, self-study, or reference in applied contexts.
Introduction.- K-Nearest Neighbors.- K-Means.- Multi-Layer Perceptrons.-
Convolutional Neural Networks.
Oliver Kramer is Professor of Computational Intelligence at the University of Oldenburg, Germany. His research focuses on evolutionary computation, machine learning, and large language modelbased cognitive architectures, with applications in optimization, bioinformatics, and artificial general intelligence. He has authored numerous books and articles and has presented his work at leading conferences such as GECCO, CEC, and ESANN. His interdisciplinary projects explore connections between AI, biology, and cognitive science.
