This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover …
Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks.
This title provides an account of a new theory and method of voting, judging and ranking, 'majority judgement', shown to be superior to all other known methods.OCLC-licensed vendor bibliographic record.
A feminist media history of quantification, uncovering the stories behind the tools and technologies we use to count, measure, and weigh our lives and realities. Anglo-American culture has used media to measure and quantify lives for centuries. Historical journal entries map the details of everyday life, while death registers put numbers to life's endings. Today we count our daily steps with fi…
"Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and comput…
" ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface.Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, ma…
The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment.What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and con…
Recently, cellular automata machines with the size, speed, and flexibility for general experimentation at a moderate cost have become available to the scientific community. These machines provide a laboratory in which the ideas presented in this book can be tested and applied to the synthesis of a great variety of systems. Computer scientists and researchers interested in modeling and simulatio…
A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.The process of inductive inference--to infer general laws and principles from particular instances--is the basis of statistical modeling, pattern recognition, and machine learning. The Minimum Descriptive Length (M…
"Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields"--OCLC-licensed vendor bibliographic record.