1. machine learning 问题的分类：
Supervised Learning: right answers given in samples
Regression: continuous result
Classification: discrete valued output
Unsupervised Learning: learning about a dataset without correct answers
Clustering: divide dataset into groups
Non-clustering: separate different voices from a voice sample (cocktail party)
2. Model Representation:
training set -> learning algorithms -> hypothesis
x -> hypothesis -> y
3. Cost Function:
m is the number of samples
4. Gradient Descent (not only for linear regression)
n is the number of features
minimization a function (ect. cost function)
the alpha is learning rate
all theta should be updated simultaneously.
5. Normal Equation Formula
comparison of gradient descent and normal equation formula.
normal equation is faster with less features.
gradient descent is faster with more features.