Coursera Machine Learning: Difference between revisions
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classification - discrete value output | classification - discrete value output | ||
m - number of training example | |||
x - input vars | |||
y - output vars | |||
(x,y) - one row of trianing | |||
(x(i),y(i)) = one training example | |||
h - hypothesis | |||
=== linear function === | |||
hΘ(x) = Θ0 + Θ1 x | |||
Latest revision as of 06:54, 9 November 2015
Administratia
Week 1
regression - continuous value output
classification - discrete value output
m - number of training example
x - input vars
y - output vars
(x,y) - one row of trianing
(x(i),y(i)) = one training example
h - hypothesis
linear function
hΘ(x) = Θ0 + Θ1 x
