Machine Learning——笔记一

1. In general, any machine learning problem can be assigned to one of two broad classifications: 

　　Supervised learning and Unsupervised learning.

 

2. Defination

Supervised learning : "right answer"given

　　- Regression : Predict continuous valued output

　　- Clasification : Discrete valued output

Unsupervised learning : "no label"

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. With unsupervised learning there is no feedback based on the prediction results.

　　- Clustering 

　　- Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment.

 

3. Linear regression