1.Supervised learning:regressionproblem,classification problem
2.unsupervisedlearning:clustering algorithm,cocktail party algorithm
3.
Hypothesis is justa standard terminology.
Cost function isalso called the squared error function or sometimes called square error costfunction
Initialize at onepoint ,step to the direction you decided that will take you downhill most quickly, until you converge to local minimum(local optimum) down here
Gradient decent algorithm
If you are alreadyat a local optimum , the derivative would be equal to zero .
And so on…
What we’re goingto do is apply gradient decent algorithm to minimize our squared error cost function
convex function only have a globaloptimum
Looking at the entirebatch of training examples
Another solution:Noramalequations methods will scale better to larger data sets
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