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1.1.2 Classification
A classification problem is like regression, except that y
(i)
is discrete. The classification
problem is binary or two-class if y
(i)
(also known as the class) is drawn from a set of two
possible values; otherwise, it is called multi-class.
1.2 Unsupervised learning
Unsupervised learning doesn’t involve learning a function from inputs to outputs based on
a set of input-output pairs. Instead, one is given a data set and generally expected to find
some patterns or structure inherent in it.
1.2.1 Density estimation
Given samples x
(
1)
, . . . , x
(n)
∈ R
d
drawn IID from some distribution Pr(X), the goal is to
IID stands for indepen-
dent and identically dis-
tributed, which means
that the elements in the
set are related in the
sense that they all come
from the same under-
lying probability distri-
bution, but not in any
other ways.
IID stands for indepen-
dent and identically dis-
tributed, which means
that the elements in the
set are related in the
sense that they all come
from the same under-
lying probability distri-
bution, but not in any
other ways.
predict the probability Pr(x
(n+
1)
)
of an element drawn from the same distribution. Density
estimation sometimes plays a role as a “subroutine” in the overall learning method for
supervised learning, as well.
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