Types of Learning

Learning with Different Output Space

Credit Approval Problem Revisited

More Binary Classification Problems

Multiclass Classification: Coin Recognition Problem

Regression: Patient Recovery Prediction Problem

Structured Learning: Sequence Tagging Problem

Learning With Different Data Label

Supervised: Coin Recognition Revisited

##　Unsupervised: Coin Recognition without $y_n$

unsupervised multiclass classification $\Leftrightarrow$‘Clustering’

challenging but useful problem

Unsupervised: Learning without $y_n$

• Clustering
• Density estimation
• Outlier Detection

Semi-supervised

Recognition with Some $y_n$

avoid expensive labeling

Reinforcement Learning

Learning with Different Protocol

Batch Learning

a very common protocol

Online: Spam Filter that ‘improves’

Active Learning: Learning by ‘Asking’

Learning with Different Input Space

Concrete Features

human intelligence

Raw Features

need human or machines to convert to concrete ones(feature engineering)

Abstract Features

again need feature conversion/extraction/construction

Summary

• Learning with Different Output Space
  ,regression,structured
• Learning with Different Data Label$y_n$
  ,un/semi-supervised,reinforecement
• Learning with Different Protocol
  ,online,active
• Learning with Different Input Space
  ,raw,abstract