Machine learning（1-Introduction）

1、What is machine learning

• Field of study that gives computers the ability to learn without being explicitly programmed

• A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E

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2、Supervised learning

• One example ：Housing price prediction

• It refers to the fact that we give the algorithm a data set in which the ''right answers'' were given. (also called a regression problem 回归问题... Regression-> continuous valued output )

• Just to produce more ''right answers''

• The other example : Breast cancer

• It refers to the fact that we give the algorithm to predict discrete set of output (Classification-> discrete valued output[0 or 1] 分类问题)
• A learning algorithm can deal with not two or three features, but an infinite number of features
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3、Unsupervised learning

• We just tell the algorithm that here is a bunch of data ，but we don't know what is in this data，who is in what type and what the different types of this data . But the algorithm can automatically find the structure of this data and cluster the individuals into these types that we don't know in advance.(There is no "right answers" for machine)
• Application:

• A example: Cocktail party problem algorithm:

   [W,s, v]= svd((repmat(sum(x .* x, 1), size (x, 1), 1).* x) *x');
  

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