Название: Creative Thinking Through Mathematics: A Student's Guide to Mathematical Explorations
Автор: Mika Munakata, Eliza Leszczyński
Издательство: Routledge
Год: 2026
Страниц: 197
Язык: английский
Формат: True PDF, True EPUB
Размер: 32.6 MB

This book engages students in interactive explorations that encourage creative thinking in their approaches to mathematics. Organized around five themes, the fifteen quests invite students to notice, wonder, and draw upon their mathematical reasoning as they connect mathematics to other disciplines and everyday situations. The quests foster students’ creativity by posing open-ended questions, encouraging reflection, inviting playfulness and curiosity, and connecting to research-based traits of creativity. The authors broaden the notion of what it means to be a mathematical thinker, provide access to mathematics through creative thinking, and celebrate the idea that everyone has the capacity to explore mathematics. Each chapter concludes with a project idea that encourages students to personalize their learning around the chapter’s theme. Accompanying worksheets and other resources are also available online. This book is written for undergraduate students, including preservice teachers, and is suitable as a textbook for general education mathematics courses.

Название: Probability and Optimization for Engineers: Probabilistic Design, Optimum Design, Finite Element Method, Artificial Intelligence
Автор: Wael A. Altabey
Издательство: De Gruyter
Год: 2026
Страниц: 229
Язык: английский
Формат: pdf (true), epub
Размер: 39.7 MB

Probability and Optimization for Engineers covers the fundamentals of probabilistic design and optimum design and methods for high performance design. It presents the principle of finite element method in probabilistic and optimum design with solved specific interactive problems of finite element analysis using ANSYS. This is the first book to use the principles of Artificial Intelligence (AI) to optimum design, offering a general introduction to the optimum design using AI algorithms such as Machine Learning, Deep Learning, artificial neural networks, Bayesian optimum design, Bayesian machine learning optimization, and genetic algorithms. The visual appeal of the book is enhanced by numerous new full-color graphic illustrations.

Название: Компьютерное моделирование финансовой деятельности
Автор: Колокольникова А. И.
Издательство: Москва
Год: 2020 - 2-е изд., испр. и доп
Cтраниц: 299 с. : ил., схем., табл.
Формат: pdf (ocr)
Размер: 30 мб
Язык: русский

Рассмотрены аналитические средства Еxcel, оптимизационное, имитационное и графическое моделирование данных, приведены примеры автоматизации финансовых экономических расчетов и исследований, компьютерная модель проектирования функциональной задачи управления.

Название: Statistical Decision Theory in Perception and Cognition: Signal Detection and General Recognition Theories
Автор: F. Gregory Ashby
Издательство: The MIT Press
Год: 2026
Страниц: 454
Язык: английский
Формат: epub (true)
Размер: 18.8 MB

A comprehensive survey of the dominant methods for separating perceptual from decisional effects and for studying perceptual interactions. Human performance in any perceptual or cognitive task can change for a variety of reasons. Signal detection theory (SDT) and its multidimensional generalization, general recognition theory (GRT), are by far the dominant methods for determining whether a change in performance is due to a change in perception or a change in how perceptual or cognitive information is used to select a response. In addition, GRT is the dominant method for studying perceptual interactions. In this book, author F. Gregory Ashby covers how SDT and GRT are used in thousands of published articles that span an enormous range of fields, including vision and all other areas of perception, memory, decision-making, eyewitness testimony, response time modeling, face perception, visual search, categorization, perceived similarity, preference, stereotyping, implicit learning, fMRI data analysis, and food science. The book includes examples that illustrate how the various methods are applied, as well as Matlab code to perform several key computations.

Название: Artificial Intelligence and Metaverse in Education
Автор: Santosh Kumar Behera, Mega Novita, Pranay Pandey, Zaffar Ahmad Nadaf, Afsana Jerin Shayery
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 622
Язык: английский
Формат: pdf (true)
Размер: 50.9 MB

Secure your place at the forefront of the academic revolution by mastering the expert-led frameworks for integrating AI and the Metaverse into immersive, ethical, and inclusive learning ecosystems that transcend physical boundaries. AI technologies, such as Machine Learning, natural language processing (NLP), and intelligent tutoring systems, are revolutionizing the ways in which educators design curricula, deliver content, and assess student progress. These tools can automate administrative tasks, provide real-time feedback, and enable adaptive learning environments that cater to individual learner needs. Simultaneously, the emergence of the metaverse—a collective virtual space combining augmented reality (AR), virtual reality (VR), and other immersive technologies—is reshaping the spatial dimensions of education. Within these digital environments, learners can engage in collaborative projects, simulate real-world scenarios, and access resources unrestricted by geographical or physical limitations.

Название: Machine Learning based Approaches for Pedagogical Data Analysis
Автор: Anirban Mukherjee, Arpan Deyasi, Soumen Mukherjee, Pampa Debnath, Lidia Ghosh
Издательство: CRC Press
Год: 2026
Страниц: 269
Язык: английский
Формат: pdf (true)
Размер: 24.3 MB

The use of intelligent technologies to enhance instruction and learning is introduced in pedagogy-based learning-teaching perspective. It covers digital library resources, AI-based tools, data analysis techniques, and NLP and NLU-powered smart assistants. Students will realize their improved efficacy through use of expandable AI systems improve educational efficiency, automate repetitive chores, and enable personalized learning. The course offers useful skills for implementing contemporary AI methods in educational institutions, classrooms, and online learning settings. This book provides concise summary of forthcoming Intelligent Tools and Techniques that are using AI-based Learning-Teaching systems to shape contemporary education. It describes how NLP and NLU applications enhance intelligent teaching assistants, showcases sophisticated library resources for promoting informal learning. The book delivers a succinct but thorough approach for implementing scalable, effective, intelligent solutions that improve learning environments across a variety of educational settings through focused insights into educational data analysis and frameworks for Expandable AI.

Название: Applications of Quantum Field Theory to Problems in Machine Learning: Advanced Techniques Based on Path Integrals
Автор: Harish Parthasarathy
Издательство: CRC Press/Manakin Press
Год: 2026
Страниц: 395
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book examines quantum neural networks through renormalization techniques, supersymmetric field theory, and noisy harmonic oscillator systems. The book's analysis covers adaptive beamforming applications, brain modeling, gravitational control mechanisms, and mixed-state dynamics in superstring theory, and also includes: - Comprehensive analysis of quantum neural networks through renormalization techniques and supersymmetric field theory applications in computational modeling; - Investigation of quantum field dynamics with noise integration, filtering mechanisms, and scattering processes in curved spacetime environments; - Study of adaptive beamforming methodologies combined with quantum neural networks for brain modeling and evolving field system applications; - Examination of mixed-state dynamics in superstring theory frameworks with emphasis on quantum noisy fields and supersymmetric effects; - Analysis of extended Kalman filter integration with quantum neural networks for transmission line control and field estimation optimization. The work explores extended Kalman filter methodologies for transmission line control, field estimation, and symmetry-broken dynamics in signal processing systems for advanced computational modeling applications.

Название: AI in Forensic Science, Data Management and Law
Автор: Christian Kaunert, Bhupinder Singh, Laeeq Razzak Janjua, Sally Lukose, Santosh Kumar
Издательство: CRC Press
Год: 2026
Страниц: 239
Язык: английский
Формат: pdf (true)
Размер: 10.4 MB

The book provides a complete overview of forensic science, AI, and data management in law and business. It reviews the applications of AI and advanced data management methods across various legal and forensic domains. Topics discussed include digital forensics, predictive modelling, automatic classification, anomaly detection, cold case analysis, metadata analysis, and pattern recognition using blockchain technology. The book explores the cutting-edge intersection of fields such as ethical AI in legal systems, forensic science, data privacy, and forensic accounting laws. It examines the ethical implications of integrating computational intelligence including AI and Machine Learning into legal and judicial systems. The challenges and opportunities of applying AI to decision-making in human rights contexts are analysed, with emphasis on transparency, accountability, and fairness in algorithmic decision-making within legal frameworks.

Название: How to See Like a Machine: Images After AI
Автор: Trevor Paglen
Издательство: Verso
Год: 2026
Страниц: 192
Язык: английский
Формат: epub (true)
Размер: 18.4 MB

We once looked at pictures. Then, with the advent of Computer Vision and Machine Learning, pictures started looking at us. By the award-winning artist, filmmaker and thinker. Today our world is under the watchful and tireless eye of Computer Vision, with cam­eras and monitors tracing our every move. Furthermore, Generative AI is now able to render a synthetic world indistinguishable from reality for us to explore. Trevor Paglen goes in search of the ways and means of understanding this new visual universe. Instead of asking what these technologies “say” about the world, he teaches us to ask what they “do” and where such images come from. In the sphere of industry, Computer Vision systems activate functions in logistics and quality control. In the world of labor, they activate automated circuits involved with hiring and human resources, employee discipline, and time management. In the area of policing, they activate surveillance and control networks through facial recognition, license plate reading, automated contraband detection, and predictive enforcement. In military domains, they automate strategic and tactical reconnaissance, target planning and acquisition, and even the firing of weapons.

Название: Artificial Intelligence in Neuroscience
Автор: Li Su
Издательство: Wiley
Год: 2026
Страниц: 367
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Provides comprehensive guidance on harnessing Artificial Intelligence for neuroscience research and clinical applications. The rapid development of Artificial Intelligence (AI) has created new opportunities for advancing the study of the brain. While recent scholarship has focused on how neuroscience can inform the design of AI systems, there is a growing need for resources that demonstrate how AI can be applied to support neuroscience research and practice. Artificial Intelligence in Neuroscience offers a detailed introduction to AI technologies and their transformative potential for fields ranging from neuroimaging and genetics to mental healthcare and neuro-oncology. AI refers broadly to computer systems designed to perform tasks that usually require human intelligence, such as learning, reasoning, problem-solving and pattern recognition. When we talk about AI in neuroscience, we are often referring specifically to Machine Learning (ML), a subfield of AI where computers automatically learn from data, improving their performance over time without needing explicit programming. Deep Learning (DL) is a more specialised form of ML, which uses multilayered neural networks loosely inspired by the structure of the human brain.