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  • 1
    Online Resource
    Online Resource
    Meet the Expert : Aileen Nielsen on Fairness in AI (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 56 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Join us for this edition of Meet the Expert with Aileen Niesen to learn how we recognize and cope with unfairness in AI. O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions. What you’ll learn and how you can apply it By the end of this live show, you’ll better understand: What fairness and unfairness in AI look like How unfairness comes about How you can address unfairness in AI This event is for you because… You want to learn about the fundamental shifts that are transforming the business landscape and customer needs Prerequisites: Come with your questions for the expert Have a pen and paper handy to capture notes, insights, and inspiration
    Note: Online resource; Title from title screen (viewed December 8, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Linear methods for optimization and prediction in healthcare : make causal inferences in health data using R and Python (2017)
    [Place of publication not identified] : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 58 min., 5 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Quantitative research ; Linear models (Statistics) ; Data mining ; Electronic videos ; local
    Abstract: "Linear methods have traditionally been the workhorse of data analysis in many domains, and health-related applications are no exception. However, linear methods have a lot more to offer than standard regression analysis. This video explains why linear thinking remains a powerful and sophisticated way to think about data for prediction, causal analysis, and optimization in health tech. Designed for data scientists and for data savvy health care managers and clinicians, it demonstrates how to strengthen the conclusions you draw from health-related data and how to better allocate your health care resources."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Deep learning for health tech : neural network applications in healthcare using Python and TensorFlow (2017)
    [Place of publication not identified] : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 33 min., 17 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Neural networks (Computer science) ; Data mining ; Quantitative research ; Electronic videos ; local
    Abstract: "Neural networks have been widely adopted across many industries as the ultimate pattern recognition tool. While their current uses in healthcare are limited, neural networks have a promising future in diagnostic and decision making applications, because of their ability to mimic--and improve on--human capabilities in health-related advice and treatment. This video explains the basics of neural networks; shows examples of training neural networks with both image-based and unstructured healthcare data; and describes the kinds of neural networks most likely to be useful for health-related applications."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Unsupervised learning for exploration and classification of health data : advanced analysis using Python and R (2017)
    [Place of publication not identified] : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (1 hr., 46 min., 43 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Data mining ; Quantitative research ; Electronic videos ; local
    Abstract: "One of the most exciting and practical goals of combining healthcare with technology is to mine large quantities of data to discover what, if anything, has eluded researchers--either through a lack of sufficiently large datasets or a lack of human ability to notice unlikely relationships. Unsupervised learning is a promising avenue for pursuing this goal, because unsupervised machine learning techniques do not require existing human knowledge to generate new insights about structure within datasets. This video, designed for learners with a basic understanding of statistics and computer programming, provides a detailed introduction to three specific types of unsupervised learning: cluster analysis, association analysis, and principal components analysis, as applied to health data sets both at the individual and population levels. Examples will be introduced in both Python and R."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    AI and machine learning for healthcare : an overview of tools and challenges for building a health-tech data pipeline (2017)
    [Place of publication not identified] : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (2 hr., 9 min., 23 sec.)) , digital, sound, color
    Keywords: Medical care ; Data processing ; Machine learning ; Artificial intelligence ; Electronic videos ; local
    Abstract: "Artificial intelligence (AI) and machine learning (ML) in healthcare comprise a rapidly expanding and very promising field. The marriage of technology and health has the potential to empower medical professionals and patients alike, while drastically cutting the cost of healthcare. Using a measured no-hype approach, this video will help technology entrepreneurs, product managers, and health care executives understand what can be done with AI and ML in healthcare today and what concepts are most crucial to producing valuable applications in the near future."--Resource description page.
    Note: Title from title screen (Safari, viewed December 6, 2017). - Release information from resource description page (Safari, viewed December 6, 2017)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Practical Fairness (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (62 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.
    Note: Online resource; Title from title page (viewed September 25, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    Practical Time Series Analysis (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (497 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
    Note: Online resource; Title from title page (viewed October 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492041641/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    Praktische Zeitreihenanalyse (2024)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    Details
    ISBN: 9781098189693 , 1098189698
    Language: Undetermined
    Pages: 1 online resource
    Note: Machine-generated record
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    Análisis Práctico de Series Temporales (2024)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    Details
    ISBN: 9781098186456 , 1098186451
    Language: Undetermined
    Pages: 1 online resource
    Note: Machine-generated record
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 10
    Online Resource
    Online Resource
    Analyse pratique des séries temporelles (2024)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    Details
    ISBN: 9798341610125
    Language: Undetermined
    Pages: 1 online resource
    Note: Machine-generated record
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
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