Bücher von Christian Servin

Meticulously crafted to align with the ACM/IEEE-CS/AAAI Computer Science CS2023 curricular guidelines, Fundamentals in Computer Programming Workbook: An Active and Guide Inquiry Learning Approach to Enhance Computational Thinking empowers students to master the art of programming. Structured to facilitate both individual and collaborative learning, this workbook guides students through the intricacies of Java programming, decision structures, loops, methods, and beyond. It delves into the realms of object-oriented programming, exception handling, recursion, and algorithmic complexities before advancing into the sophisticated territories of arrays, linked lists, stacks, queues, trees, heaps, and hash tables. With a focus on active learning, the workbook employs Bloom's for Computing revised taxonomy to ensure a robust development of computational thinking skills. Each chapter is a step-by-step journey through problem-solving, supported by digital content accessible via QR codes for a seamless blend of traditional and modern learning experiences. Designed for a diverse range of learners-from community colleges to technical schools to four-year institutions-Fundamentals in Computer Programming Workbook is the perfect companion for undergraduate computer science courses and programs.

On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing uncertainty, and how to make these algorithmsmore computationally efficient. It explains in what sense the existing approach touncertainty as a combination of random and systematic components is only anapproximation, presents a more adequate three-component model with an additionalperiodic error component, and explains how uncertainty propagation techniques canbe extended to this model. The book provides a justification for a practically efficientheuristic technique (based on fuzzy decision-making). It explains how the computationalcomplexity of uncertainty processing can be reduced. The book also shows how totake into account that in real life, the information about uncertainty is often onlypartially known, and, on several practical examples, explains how to extract the missinginformation about uncertainty from the available data.