Librarian
Research Starters for the Computational Data Sciences Option
-
IEEE Xplore This link opens in a new windowIEEE Xplore contains the full text of all IEEE & IET conference papers, and standards published since 1988 and all IEEE journals since 1884 & IET journals since 1988. The material can be accessed through a searchable database or by browsing. 100 concurrent users. Inactive users timeout after 15 minutes.
-
Data Mining and Knowledge DiscoveryData Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques. The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.
-
Data Science & EngineeringData Science and Engineering publishes original research documents in the field of Databases & Information Systems and Machine Learning & Artificial intelligence.
-
Machine LearningMachine Learning is an international forum for research on computational approaches to learning. The journal features papers that describe research on problems and methods, applications research, and issues of research methodology.
Books on Machine Learning
-
AI for Robotics [electronic resource] : Toward Embodied and General Intelligence in the Physical World
by
This book approaches robotics from a deep learning perspective. Artificial intelligence (AI) has transformed many fields, including robotics. This book shows you how to reimagine decades-old robotics problems as AI problems and is a handbook for solving problems using modern techniques in an era of large foundation models. The book begins with an introduction to general-purpose robotics, how robots are modeled, and how physical intelligence relates to the movement of building artificial general intelligence, while giving you an overview of the current state of the field, its challenges, and where we are headed. The first half of this book delves into defining what the problems in robotics are, how to frame them as AI problems, and the details of how to solve them using modern AI techniques. First, we look at robot perception and sensing to understand how robots perceive their environment, and discuss convolutional networks and vision transformers to solve robotics problems such as segmentation, classification, and detection in two and three dimensions. The book then details how to apply large language and multimodal models for robotics, and how to adapt them to solve reasoning and robot control. Simulation, localization, and mapping and navigation are framed as deep learning problems and discussed with recent research. Lastly, the first part of this book discusses reinforcement learning and control and how robots learn via trial and error and self-play. The second part of this book is concerned with applications of robotics in specialized contexts. You will develop full stack knowledge by applying the techniques discussed in the first part to real-world use cases. Individual chapters discuss the details of building robots for self-driving, industrial manipulation, and humanoid robots. For each application, you will learn how to design these systems, the prevalent algorithms in research and industry, and how to assess trade-offs for performance and reliability. The book concludes with thoughts on operations, infrastructure, and safety for data-driven robotics, and outlooks for the future of robotics and machine learning. In summary, this book offers insights into cutting-edge machine learning techniques applied in robotics, along with the challenges encountered during their implementation and practical strategies for overcoming them. What You Will Learn Explore ML applications in robotics, covering perception, control, localization, planning, and end-to-end learning Delve into system design, and algorithmic and hardware considerations for building efficient ML-integrated robotics systems Discover robotics applications in self-driving, manufacturing, and humanoids and their practical implementations Understand how machine learning and robotics benefit current research and organizations .
ISBN: 9798868809897Publication Date: 2025 -
Mitigating Bias in Machine Learning
by
This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
ISBN: 9781264922444Publication Date: 2024-10-02 -
From Algorithms to Thinking Machines
by
This book introduces and provides an analysis of the basic concepts of algorithms, data, and computation and discusses the role of algorithms in ruling and shaping our world. It provides a clear understanding of the power and impact on humanity of the pervasive use of algorithms. From Algorithms to Thinking Machines combines a layman's approach with a well-founded scientific description to discuss both principles and applications of algorithms, Big Data, and machine intelligence. The book provides a clear and deep description of algorithms, software systems, data-driven applications, machine learning, and data science concepts, as well as the evolution and impact of artificial intelligence. After introducing computing concepts, the book examines the relationships between algorithms and human work, discussing how jobs are being affected and how computers and software programs are influencing human life and the labor sphere. Topics such as value alignment, collective intelligence, Big Data impact, automatic decision methods, social control, and political uses of algorithms are illustrated and discussed at length without excessive technical detail. Issues related to how corporations, governments, and autocratic regimes are exploiting algorithms and machine intelligence methods to influence people, laws, and markets are extensively addressed. Ethics principles in software programming and human value insertion into artificial intelligence algorithms are also discussed.
ISBN: 9798400708558Publication Date: 2023-09-25 -
Algorithmic Training, Future Markets, and Big Data for Finance Digitalization
by
Algorithmic training, future markets, and big data are at the forefront of the digitalization revolution in finance, driving innovation and reshaping the industry's landscape. The integration of algorithms in financial decision-making enables faster, accurate predictions and automated processes, optimizing trading strategies and risk management. As financial markets evolve, future-oriented technologies, including artificial intelligence and machine learning, offer new areas for growth, with algorithms refining their effectiveness in response to real-time data. Big data provides vast amounts of information that can be analyzed to identify trends, enhance customer experiences, and inform investment strategies. Together, these elements transform finance into a more agile, data-driven system, increasing digitalization and creating new opportunities for growth, efficiency, and innovation. Algorithmic Training, Future Markets, and Big Data for Finance Digitalization explores the most current and transformative trends in the world of digital finance, from digital currencies to blockchain, fintech, financial inclusion and artificial intelligence. It offers a comprehensive analysis of how technology is revolutionizing financial services, products, and access for individuals and institutions alike. This book covers topics such as data science, financial technology, and machine learning, and is a useful resource for business owners, computer engineers, entrepreneurs, economists, finance professionals, academicians, and researchers.
ISBN: 9798369363874Publication Date: 2025-01-08
Books on Data Structures
-
C# Data Structures and Algorithms
by
Write sophisticated C# code with this complete guide to using diverse data structures and algorithms, featuring ready-to-use code snippets, detailed explanations, and illustrations Key Features Master lists, stacks, queues, dictionaries, sets, and trees, among other data structures Delve into effective design and implementation techniques to meet your software requirements Visualize data structures and algorithms through illustrations for a clearer understanding of their analysis Purchase of the print or Kindle book includes a free PDF eBook Book Description Building your own applications is exciting but challenging, especially when tackling complex problems tied to advanced data structures and algorithms. This endeavor demands profound knowledge of the programming language as well as data structures and algorithms. C# Data Structures and Algorithms, armed with critical knowledge from an entrepreneur, author, industry expert, and seasoned developer with significant international experience, offers just that to C# developers.Starting with an introduction to algorithms, this book gradually immerses you in the world of arrays, lists, stacks, queues, dictionaries, and sets. Real-world examples, enriched with code snippets and illustrations, provide a practical understanding of these concepts. You'll also learn how to sort arrays using various algorithms, setting a solid foundation for your programming expertise. As you progress, you'll venture into more complex data structures - trees and graphs - and discover algorithms for tasks such as determining the shortest path in a graph before advancing to see various algorithms in action, such as solving Sudoku.By the end of this book, you'll be able to use the C# language to build algorithmic components that are not only easy to understand and debug but also seamlessly applicable in various apps, spanning web and mobile platforms. What you will learn Understand the fundamentals of algorithms and their classification Store data using arrays and lists, and explore various ways to sort arrays Build enhanced applications with stacks, queues, hashtables, dictionaries, and sets Create efficient applications with tree-related algorithms, such as for searching in a binary search tree Boost solution efficiency with graphs, including finding the shortest path in the graph Implement algorithms solving Tower of Hanoi and Sudoku games, generating fractals, and even guessing the title of this book Who this book is for This book is for developers looking to learn data structures and algorithms in C#. While basic programming skills and C# knowledge is useful, beginners will find value in the provided code snippets, illustrations, and detailed explanations, enhancing their programming skills. Advanced developers can use this book as a valuable resource for reusable code snippets, instead of writing algorithms from scratch each time. ]]>
ISBN: 9781803248271Publication Date: 2024-02-29 -
Data Structures in Depth Using C++
by
Understand and implement data structures and bridge the gap between theory and application. This book covers a wide range of data structures, from basic arrays and linked lists to advanced trees and graphs, providing readers with in-depth insights into their implementation and optimization in C++. You'll explore crucial topics to optimize performance and enhance their careers in software development. In today's environment of growing complexity and problem scale, a profound grasp of C++ data structures, including efficient data handling and storage, is more relevant than ever. This book introduces fundamental principles of data structures and design, progressing to essential concepts for high-performance application. Finally, you'll explore the application of data structures in real-world scenarios, including case studies and use in machine learning and big data. This practical, step-by-step approach, featuring numerous code examples, performance analysis and best practices, is written with a wide range of C++ programmers in mind. So, if you're looking to solve complex data structure problems using C++, this book is your complete guide. What You Will Learn Write robust and efficient C++ code. Apply data structures in real-world scenarios. Transition from basic to advanced data structures Understand best practices and performance analysis. Design a flexible and efficient data structure library. Who This Book is For Software developers and engineers seeking to deepen their knowledge of data structures and enhanced coding efficiency, and ideal for those with a foundational understanding of C++ syntax. Secondary audiences include entry-level programmers seeking deeper dive into data structures, enhancing their skills, and preparing them for more advanced programming tasks. Finally, computer science students or programmers aiming to transition to C++ may find value in this book.
ISBN: 9798868808012Publication Date: 2025-03-07 -
Absolute Beginner's Guide to Algorithms
by
A hands-on, easy-to-comprehend guide that is perfect for anyone who needs to understand algorithms. With the explosive growth in the amount of data and the diversity of computing applications, efficient algorithms are needed now more than ever. Programming languages come and go, but the core of programming--algorithms and data structures--remains the same. Absolute Beginner's Guide to Algorithms is the fastest way to learn algorithms and data structures. Using helpful diagrams and fully annotated code samples in Javascript, you will start with the basics and gradually go deeper and broader into all the techniques you need to organize your data. Start fast with data structures basics: arrays, stacks, queues, trees, heaps, and more Walk through popular search, sort, and graph algorithms Understand Big-O notation and why some algorithms are fast and why others are slow Balance theory with practice by playing with the fully functional JavaScript implementations of all covered data structures and algorithms Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
ISBN: 9780138222291Publication Date: 2023-11-24 -
Data Structures and Algorithms with the C++ STL
by
No detailed description available for "Data Structures and Algorithms with the C++ STL".
ISBN: 9781835469071Publication Date: 2024-03-14
