Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.
Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
The leading traits of executives often include creativity and innovation. Research shows that intuition can significantly enhance these traits. Developing intuitive executives and honing intuition, coupled with the ability to apply data and evidence to inform decision making, is the focus of Developing the Intuitive Executive: Using Analytics and Intuition for Success. Some researchers call the complement of applying data analytics to intuition as quantitative intuition, rational intuition, or informed intuition. Certainly, in today's data-driven environment, analytics plays a key role in executive decision-making. However, an executive’s many years of experiential learning are not formally considered as part of the decision-making process. Learning from both failures and successes can help fine-tune intuitive awareness—what this book calls intuition-based decision-making. Research also shows that many executives do not trust the internal data quality in their organizations, and so they rely on their intuition rather than strictly on data. This book presents the work of leading researchers worldwide on intuition in the management and executive domain. Their chapters cover key issues, trends, concepts, techniques, and opportunities for applying intuition as part of the executive decision-making process. Highlights include: Using intuition to manage new opportunities Intuition in medicine Rules based on intuition Balancing logic and intuition in decision-making Smart heuristics to manage complexity Intuition and competitiveness Intuitive investment decision-making across cultures Showing how intuition in executive decision-making should play an important role, this book enables managers to complement their knowledge gained from experience with analytics to improve decision-making and business success.
Discover the extraordinary possibilities of machine learning and artificial intelligence in this groundbreaking exploration. From self-driving cars to virtual assistants, this book delves into the fascinating world of algorithms and how they are transforming industries and revolutionizing our lives. Explore the inner workings of neural networks, deep learning models, and predictive analytics, and witness the profound impact they have on decision-making, problem-solving, and data analysis. Whether you're a novice or an expert in the field, prepare to be captivated by the limitless potential of machine learning and AI.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.