MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of
experts in the field, this collection of papers reflects the most
up-to-date and comprehensive current state of machine learning and
data science for industry, government, and academia. Machine
learning (ML) and data science (DS) are very active topics with an
extensive scope, both in terms of theory and applications. They
have been established as an important emergent scientific field and
paradigm driving research evolution in such disciplines as
statistics, computing science and intelligence science, and
practical transformation in such domains as science, engineering,
the public sector, business, social science, and lifestyle.
Simultaneously, their applications provide important challenges
that can often be addressed only with innovative machine learning
and data science algorithms. These algorithms encompass the larger
areas of artificial intelligence, data analytics, machine learning,
pattern recognition, natural language understanding, and big data
manipulation. They also tackle related new scientific challenges,
ranging from data capture, creation, storage, retrieval, sharing,
analysis, optimization, and visualization, to integrative analysis
across heterogeneous and interdependent complex resources for
better decision-making, collaboration, and, ultimately, value
creation.
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