Types of Learning
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’:a challenging but useful problem
Unsupervised: Learning without $y_n$
- Clustering
- Density estimation
- Outlier Detection
Semi-supervised:Coin 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:classification,regression,structured
- Learning with Different Data Label$y_n$:supervised,un/semi-supervised,reinforecement
- Learning with Different Protocol:batch,online,active
- Learning with Different Input Space:concrete,raw,abstract
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