Machine Learning Notes
Machine Learning Notes
this is the summary: courses of ML on cousera
by Andrew Ng
1.What is Machine Learning?
**Definition:**A computer program is said to learn from experience E with respect to some tasks T and some performance measure P,if its performance on T,as measured by P,improves with experience E. E
:test data,learning process P
:the evaluation/summary of learning,prediction by this program is accuracy/correct or not. T
:The goal we want to achieve.
2.Classification
-
Supervised Learning
Given the right/exact anwser for each example in the data.- Regresstion: estimate the relationships among variables with continuous output.
- Classification: identify which category an example belongs to with discrete output.
-
Unsupervised Learning
allow us to approach problems with little or no idea what our results should like.
3.Model Representation
a training set
–learning algorithm
–>h
(hypothesis)
After that, we use this h
to predict y
with x
4.Cost Function
What you should always keep in mind is that function J is parametered by theta rather than x
or y
.
5.Gradient Descent
We have put forward the goal we are going to do: minimize the function J.
BUT how to achieve that?
There are two ways in linear regresstion.And now let’t introduce the first one: Gradient Descent
algorithm:
Attention: At each iteration,one should simutaneouly updata the parameters theta.
Batch gradient descent: this method looks at every example in the entire training set on every step.