Machine learning preface

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