There is an established history of computational learning by fitting models to data. Previously the purview of statisticians, these models help us to understand complex problems by identifying patterns in data that are otherwise unnoticeable to humans. These models are typically associative in design, in that correlations do not necessarily imply causation. Given the well-described limitations of statistical models, there is a healthy scepticism of these approaches. Despite an awareness of these limitations, humans seem hard-wired to see a causal paradigm in mathematical models [2].
learning from data pdf download
AI and ML models are a set of methods for learning patterns from data. Albeit optimised for different scenarios, statistical and ML approaches both share the same goal. AI models emphasise predictive accuracy, typically in large datasets, without a particular focus on inference for any one individual predictor. Statistical models stress a direct analytical approach, which characterises with uncertainty, estimators for individual predictors [3]. Statistical models tend to provide parameters that have a more directly interpretable human meaning. This ease of interpretability can make statistical models less able to describe complex phenomena. They are often either intractable at scale, or become powered to detect clinically meaningless signals. With these shortcomings in mind, AI models have enabled a new branch of learning from massive datasets.
The introduction discusses the idea of data journeys and its characteristics as an investigative tool and theoretical framework for this volume and broader scholarship on data. Building on a relational and historicized understanding of data as lineages, it reflects on the methodological and conceptual challenges involved in mapping, analyzing and comparing the production, movement and use of data within and across research fields and approaches, and the strategies developed to cope with such difficulties. The introduction then provides an overview of significant variation among data practices in different research areas that emerge from the analyses of data journeys garnered in this volume. In closing, it discusses the significance of this approach towards addressing the challenges raised by data-centric science and the emergence of big and open data.
This article examines the use of interim assessments in elementary schools in the School District of Philadelphia. The article reports on the qualitative component of a multimethod study about the use of interim assessments in Philadelphia. The study used an organizational learning framework to explore how schools can best develop the capacity to utilize the potential benefits of interim assessments. The qualitative analysis draws on data from intensive fieldwork in 10 elementary schools and interviews with district staff and others who worked with the schools, as well as further in-depth case study analysis of 5 schools. This article examines how school leaders and grade groups made sense of data provided through interim assessments and how they were able to use these data to rethink instructional practice. We found substantial evidence that interim assessments have the potential to contribute to instructional coherence and instructional improvement if they are embedded in a robust feedback system. Such feedback systems were not the norm in the schools in our study, and their development requires skill, knowledge, and concerted attention on the part of school leaders.
The rise of dominant firms in data driven industries is often credited to their alleged data advantage. Empirical evidence lending support to this conjecture is surprisingly scarce. In this paper we document that data as an input into machine learning tasks display features that support the claim of data being a source of market power. We study how data on keywords improve the search result quality on Yahoo!. Search result quality increases when more users search a keyword. In addition to this direct network effect caused by more users, we observe a novel externality that is caused by the amount of data that the search engine collects on the particular users. More data on the personal search histories of the users reinforce the direct network effect stemming from the number of users searching the same keyword. Our findings imply that a search engine with access to longer user histories may improve the quality of its search results faster than an otherwise equally efficient rival with the same size of user base but access to shorter user histories.
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate.
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
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Welcome Message from the AuthorsMachine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover.
Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems.
Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own.
Although high-throughput first principles-based methods have shown promise in the design of NCS half-Heusler alloys13, exhaustive calculations for more complex crystal structures with numerous polymorphs (such as the RPs) and thousands of unexplored chemical compositions have not (yet) been demonstrated. This is partly because the potential energy surface of complex oxides is difficult to navigate. Phonon instabilities at high-symmetry points away from the Γ-point in the irreducible Brillouin zones cause the primitive unit cell to multiply several fold, resulting in large system sizes and vast numbers of unique atomic arrangements. It is challenging to rigorously evaluate the energetics of all structures in a high-throughput manner. Furthermore, chemistries with partially filled d (and/or f) orbitals and the existence of energetically competing ground states complicate the structure prediction process. As a result, novel approaches are desired to guide the first principles calculations in an effective manner. Materials informatics, a growing field at the intersections of many scientific disciplines including data and information science, statistics, machine learning (ML) and optimization, has the potential to accomplish this objective14. 2ff7e9595c
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