Machine learning Preface

Definition

• T: Task
• E: Experience
• P: Performance
• Sequence: T -> E -> P

Supervised learning

Definition

• Give the right answer to each example of the data set(called training data).

Type

• Regression: get the continuous values
• Classification: get the discrete values like 0, 1, 2, 3 and so on

application scenarios

• Regression: predict the price of the house based on the square, location of the house
  • house price
• Classification:
  • Tumor prediction
  • Spam filter

Unsupervised learning

Type

• Cluster algorithm

application scenarios

• Google news: get lots of related news in the Internet and put them in one set of URL.
• Social network: find the common friends.
• Market segmentation: We all know the data, but we don‘t know the what kinds of market segmentation, so let unsupervised learning to deal with it.
• Extract human voice from records: you know, there are some noise in these records, we need to get the human voice, so we let cluster algorithm to deal with.

Others

Recommender system

Ng Machine learning

原文：https://www.cnblogs.com/megachen/p/9903276.html