| dc.description.abstract | The field of Statistics is constantly challenged by the problems that science
and industry brings to its door. In the early days, these problems often came
from agricultural and industrial experiments and were relatively small in
scope. With the advent of computers and the information age, statistical
problems have exploded both in size and complexity. Challenges in the
areas of data storage, organization and searching have led to the new field
of “data mining”; statistical and computational problems in biology and
medicine have created “bioinformatics.” Vast amounts of data are being
generated in many fields, and the statistician’s job is to make sense of it
all: to extract important patterns and trends, and understand “what the
data says.” We call this learning from data.
The challenges in learning from data have led to a revolution in the statistical
sciences. Since computation plays such a key role, it is not surprising
that much of this new development has been done by researchers in other
fields such as computer science and engineering.
The learning problems that we consider can be roughly categorized as
either supervised or unsupervised. In supervised learning, the goal is to predict
the value of an outcome measure based on a number of input measures;
in unsupervised learning, there is no outcome measure, and the goal is to
describe the associations and patterns among a set of input measures. | en_US |
