## Learning with Different Output Space $\gamma$

### Binary classification

Y = {−1, +1}

### Multiclass Classification

- classify US coins (1c, 5c, 10c, 25c) by (size, mass)
- $\gamma = {1c,5c,10c,25c}$,or $\gamma = {1,2,··· ,K}$ (abstractly)
- binary classification: special case with K =2

### Regression

- $\gamma = \mathbb{R}$ or $\gamma = [lower, upper] \subset \mathbb{R}$ (bounded regression)

### Structured Learning: Sequence Tagging Problem

- a fancy but complicated learning problem
- sentence -> structure (class of each word)
- $\gamma = \{PVN,PVP,NVN,PV,···\}$, not including VVVVV
- huge multiclass classification problem ($structure \equiv hyperclass$) without
**explicit**class definition

## Learning with Different Data Label $y_n$

### Supervised learning

- every $x_n$ comes with corresponding $y_n$

### Unsupervised learning

- clustering
- articles -> topics
- consumer profiles -> consumer groups

- density estimation: {xn} -> density(x)
- i.e. traffic reports with location -> dangerous areas

- outlier detection: {xn} -> unusual(x)
- i.e. Internet logs -> intrusion alert

### Semi-supervised learning

- leverage unlabeled data to avoid
**expensive**labeling

### Reinforcement Learning

- Teach Your Dog: Say
**Sit Down**- cannot easily show the dog that $y_n$ = sit when $x_n$ = sit down
- but can
**punish**to say $\hat{y_n}$ = pee is wrong - but can reward to say $\hat{y_n}$ = sit is good

- learn with partial/implicit information (often sequentially)

## Learning with different Protocol $f \rightarrow (x_n,y_n)$

### Batch Learning

- batch supervised multiclass classification: learn from all known data

### Online Learning:

- hypothesis
**improves**through receiving data instances sequentially

### Active Learning: Learning by ‘Asking’

- improve hypothesis with fewer labels (hopefully) by asking questions strategically

## Learning with different Input Space $\chi$

- concrete features: each dimension of $\chi \in \mathbb{R}$ represents
**sophisticated physical meaning** - Raw Features
- simple physical meaning; thus more difficult for ML than concrete features
- often need human or machines to convert to concrete ones

- Abstract Features: again need
**feature conversion/extraction/construction**