Artificial Intelligence In Mathematics
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Mathematics Education In The Age Of Artificial Intelligence
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Author : Philippe R. Richard
language : en
Publisher: Springer
Release Date : 2022-03-15
Mathematics Education In The Age Of Artificial Intelligence written by Philippe R. Richard and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Education categories.
This book highlights the contribution of artificial intelligence for mathematics education. It provides concrete ideas supported by mathematical work obtained through dynamic international collaboration, and discusses the flourishing of new mathematics in the contemporary world from a sustainable development perspective. Over the past thirty years, artificial intelligence has gradually infiltrated all facets of society. When it is deployed in interaction with the human designer or user, AI certainly raises new ethical questions. But as soon as it aims to augment intelligence in a kind of human-machine partnership, it goes to the heart of knowledge development and the very performance of work. The proposed themes and the sections of the book address original issues relating to the creation of AI milieus to work on mathematics, to the AI-supported learning of mathematics and to the coordination of « usual » paper/pencil techniques and « new » AI-aided educational working spaces. The authors of the book and the coordinators of each section are all established specialists in mathematics didactics, mathematics and computer science. In summary, this book is a must-read for everyone interested in the teaching and learning of mathematics, and it concerns the interaction between the human and the machine in both directions. It contains ideas, questions and inspiration that invite to take up the challenge of Artificial Intelligence contributing to Mathematical Human Learning.
Mathematics And Education In An Ai Era
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Author : Dragana Martinovic
language : en
Publisher: Springer Nature
Release Date : 2025-04-25
Mathematics And Education In An Ai Era written by Dragana Martinovic and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-25 with Education categories.
This book focuses on the potential contributions of Artificial Intelligence (AI) for enhancing mathematics education. It includes rationales for an AI-oriented pedagogical model, such as interdisciplinarity and even sensitivity to crucial world issues, such as climate change. The chapters in this book highlight what the new age of mathematics education entails concretely, covering themes from the utilization of AI directly into classroom pedagogy and the semiotic consequences of what this entails, to how mathematics training can be tailored to get students to relate concretely to problems of climate change, and to understand the relevance of the differences between symmetry and asymmetry as psychological constructs. The overall picture we can glean from these chapters is not mere eclecticism, but an integration of disciplinary perspectives into a holistic framework that has great relevance and resonance for mathematics education in the age of AI.
Artificial Intelligence In Mathematics
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Author : Jeffrey Johnson
language : en
Publisher: Oxford University Press, USA
Release Date : 1994
Artificial Intelligence In Mathematics written by Jeffrey Johnson and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
This book offers a revelatory glimpse into the future--when science, social science, and social administration will be based on the complementary interplay between artificial intelligence, mathematics, and statistics. Comprised of contributions from a broad range of leading scientists and researchers, the book outlines how artificial intelligence supplies insights into the nature of complex problems, mathematics offers a rich language for presenting systems and methods for investigating them rigorously, and statistics provides the interface between theory and data from both observation and experiment. Students and researchers in applied mathematics, artificial intelligence, and statistics interested in the growing integration of computer technologies and modern mathematical breakthroughs will want to read this important new book.
Computational Intelligence And Mathematics For Tackling Complex Problems 3
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Author : István Á. Harmati
language : en
Publisher: Springer
Release Date : 2021-08-26
Computational Intelligence And Mathematics For Tackling Complex Problems 3 written by István Á. Harmati and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-26 with Technology & Engineering categories.
Complex problems and systems, which prevail in the real world, cannot often be tackled and solved either by traditional methods offered by mathematics or even the traditional computer science (CS) and and artificial intelligence (AI)..). What is the way out of this dilemma? Advanced methodologies, and tools and techniques, „mimicking” human reasoning or the behavior of animals, animal populations or certain parts of the living bod, based on traditional computer science science and the initial approaches of artificial intelligence are often referred to as biologically inspired methods, or often computational intelligence (CI). Computational intelligence offers effective and efficient solutions to many „unsolvable" problems problems. However, it is far from being a ready to use and complete collection of approaches, and is rather a continuously developing field without clear borders. The emerging new models and algorithms of computational intelligence are deeply rooted in the vast apparatus of traditional mathematics. Thus, the investigation of connections and synergy between mathematics and computational intelligence is an eminent goal which is periodically pursued by a group of mathematicians and computational intelligence researchers who regularly attand the annual European Symposia on Computational Intelligence and Mathematics (ESCIM). Some relevant papers from the last ESCIM-2020 are included in this volume.
Recent Advances In Intelligent Information Systems And Applied Mathematics
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Author : Oscar Castillo
language : en
Publisher: Springer Nature
Release Date : 2020-01-31
Recent Advances In Intelligent Information Systems And Applied Mathematics written by Oscar Castillo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Technology & Engineering categories.
This book describes the latest advances in intelligent techniques such as fuzzy logic, neural networks, and optimization algorithms, and their relevance in building intelligent information systems in combination with applied mathematics. The authors also outline the applications of these systems in areas like intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. By sharing fresh ideas and identifying new targets/problems it offers young researchers and students new directions for their future research. The book is intended for readers from mathematics and computer science, in particular professors and students working on theory and applications of intelligent systems for real-world applications.
Artificial Intelligence Engines
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Author : James V Stone
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-25
Artificial Intelligence Engines written by James V Stone and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-25 with Computers categories.
Dive into the fundamentals of artificial intelligence engines, covering deep learning and its mathematical underpinnings. Practical examples and tutorials help bridge the gap between theoretical AI concepts and applications. Key Features Comprehensive breakdown of foundational and advanced AI algorithms with practical insights. Clear explanations of deep learning architectures, including GANs, autoencoders, and CNNs. Step-by-step guidance on implementing mathematical concepts in real-world AI applications. Book DescriptionThis book is a comprehensive guide to the mathematics behind artificial intelligence engines, taking readers from foundational concepts to advanced applications. It begins with an introduction to artificial neural networks, exploring topics like perceptrons, linear associative networks, and gradient descent. Practical examples accompany each chapter, making complex mathematical principles accessible, even for those with limited prior knowledge. The book's detailed structure covers key algorithms like backpropagation, Hopfield networks, and Boltzmann machines, advancing to deep restricted Boltzmann machines, variational autoencoders, and convolutional neural networks. Modern topics such as generative adversarial networks, reinforcement learning, and capsule networks are explored in depth. Each section connects theory to real-world AI applications, helping readers understand how these techniques are used in practice. Ideal for students, researchers, and AI enthusiasts, the book balances theoretical depth with practical insights. Basic mathematical knowledge or foundation is recommended, allowing readers to fully engage with the content. This book serves as an accessible yet thorough resource for anyone eager to dive deeper into artificial intelligence and machine learning.What you will learn Master the fundamentals of artificial neural networks effectively. Apply gradient descent techniques for training neural networks. Design perceptrons to solve classification and logic problems. Explore the mechanics of backpropagation in learning processes. Analyze Boltzmann machines for generative model applications. Examine reinforcement learning for decision-making scenarios. Who this book is for Students and professionals interested in artificial intelligence and machine learning will find this book an invaluable resource. A basic mathematical knowledge or foundation is recommended to follow the concepts effectively. Suitable for researchers, engineers, and AI enthusiasts aiming to strengthen their theoretical understanding while gaining practical implementation skills.
Math For Machine Learning
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Author : Richard Han
language : en
Publisher:
Release Date : 2018
Math For Machine Learning written by Richard Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science, Data Science. Artificial Intelligence. If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable--courses in linear algebra, discrete math, and calculus."--Resource description page.
Hands On Mathematics For Deep Learning
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Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12
Hands On Mathematics For Deep Learning written by Jay Dawani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Computers categories.
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Machine Learning Math
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Author :
language : en
Publisher:
Release Date : 2020-05-21
Machine Learning Math written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-21 with categories.
Are you looking for a complete guide of machine learning? Then keep reading... In this book, you will learn about the OpenAI Gym, used in reinforcement learning projects with several examples of the training platform provided out of the box. Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies. The book is perfect for all people who have a desire to study data science. Have you heard of machine learning being everywhere, and you intend to understand what it can do? Or are you familiar with applying the tools of machine learning, but you want to make sure you aren't missing any? Having a little knowledge about mathematics, statistics, and probability would be helpful, but this book has been written in such a way that you will get most of this knowledge as you continue reading. You should not shy away from reading the book if you have no background in machine learning. You will learn how to use reinforcement learning algorithms in other tasks, for example, the board game Go, and generating deep image classifiers. This will help you to get a comprehensive understanding of reinforcement learning and help you solve real-world problems. The most interesting part of this book is the asynchronous reinforcement learning framework. You will learn what the shortcomings of DQN are, and why DQN is challenging to apply in complex tasks. Then, you will learn how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm. You will learn four important things. The first one is how to implement games using gym and how to play games for relaxation and having fun. The second one is that you will learn how to preprocess data in reinforcement learning tasks such as in computer games. For practical machine learning applications, you will spend a great deal of time understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You will learn the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you will learn how to implement DQN using TensorFlow and how to visualize the training process. The following is a glimpse of what you will find inside the book: Introduction to machine learning The best machine learning algorithms Regression (a problem of predicting a real-valued label) and classification( a problem of automatically assigning a label to unlabeled example-for example spam detection) Reinforcement learning Robotics Supervised and Unsupervised learning How to implement a convolutional neural network(usually used for images) in TensorFlow Deep Learning Data preparation and processing TensorFlow machine learning frameworks Neural Networks (a combination of linear and non-linear functions) Clustering(aims to group similar samples together) Even if you have never studied Machine Learning before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!
Ai For Math Teachers
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Author : K Star
language : en
Publisher: Independently Published
Release Date : 2025-02-22
Ai For Math Teachers written by K Star and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-22 with Education categories.
Unlock the Future of Math Education with AI! Are you ready to revolutionize your math classroom? "AI for Math Teachers: Transforming Mathematics Education with Artificial Intelligence" is your ultimate guide to harnessing the power of AI to enhance teaching, engage students, and simplify administrative tasks. This comprehensive e-book is designed specifically for math teachers who want to stay ahead of the curve and prepare their students for a technology-driven world. Whether you're new to AI or looking to deepen your expertise, this resource provides practical strategies, real-world examples, and actionable insights to help you integrate AI seamlessly into your teaching practice. What You'll Learn: The Basics of AI in Education: Understand how AI works and why it's a game-changer for math teachers. AI Tools for Every Need: Discover the best AI-powered tools for personalized learning, problem-solving, content creation, and administrative efficiency. Personalized Learning: Learn how to use AI to tailor instruction to individual student needs and track progress effectively. Engaging Your Classroom: Explore gamification, virtual reality, and AI chatbots to make math more interactive and fun. Ethical Considerations: Navigate the challenges of data privacy, bias, and accessibility to ensure responsible AI use. Future-Proof Your Teaching: Stay ahead with insights into emerging trends like predictive analytics, AI-driven insights, and immersive learning experiences. Why This E-Book? Practical and Actionable: Step-by-step guidance on choosing, implementing, and optimizing AI tools in your classroom. Real-World Examples: Case studies from math teachers who have successfully integrated AI into their teaching. Comprehensive Resources: A curated list of AI tools, professional development opportunities, and research to support your journey. Future-Focused: Prepare your students for an AI-driven world by teaching computational thinking, coding, and AI literacy. Who Is This For? Math Teachers (K-12 and Beyond): Whether you teach elementary algebra or advanced calculus, this e-book is tailored to your needs. Educators Embracing Technology: If you're curious about AI but unsure where to start, this guide will demystify the process. School Leaders and Administrators: Equip your teachers with the knowledge and tools to transform math education in your school. Transform Your Classroom Today! AI is no longer a futuristic concept-it's here, and it's reshaping education. By embracing AI, you can save time, engage students, and unlock their full potential in math. Don't get left behind. Download "AI for Math Teachers" now and start your journey toward a smarter, more innovative classroom!