Any machine learning can be thought as 3 parts .
(1) Experience ，E means the machine learn from what experience (In chinese means 需要怎样的学习过程)
(2)P,P means the performance measure. (In chinese means 测量这个结果的好坏 )
(3)Task,T just means the task . ( In chinese means 需要完成的目标 )
Machine learning can be divided into 2 parts .
(1)supervised learning
supervised learning means we have the data set {xi yi}(i=1~n)
supervised learning includes regression and classification .
regression means we must get the actually input output function which is continual .
classification means
we must get the actually input output function which is discret .
(2)unsupervised learning
unsupervised learning means we have the data set {xi }(i=1~n)
unsupervised learning includes the cluster problem.
automatically divides the data into several parts .
Hypothesis
Hypothesis
Cost function .
Hypothesis and the actual model . I would take an example .
error square function which is a kind of cost function .
m : the number of the data set
: the hypothesis
:one of the element of the data set .
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My mistake ， this function is called square error cost function
