Machine Learning from Perspective of Linear Algebra
Linear albegra world view
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Every 'object' can be represented by a vector, even if it has uncountably many features.
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Conversely, once we decide on a set of features, every object can be represented by a vector.
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Thus, once we decide on a set of features, every object is comparable to every object, by a simple dot product.
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A property is derived by multiplying the vector representing the object by a matrix corresponding to that property.
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A set of features defines a characterization of the object.
Concrete examples:
Learning
Given y = f(x)
and a 'large enough' distribution of (x, y)
,
it is possible to find g
such that for 'almost all' x
, y = g(x)
holds.
Concrete examples: