这一讲，林老师从4个不同的角度对机器学习算法做分类。

1、Learning with Different Output Space $\mathcal{Y}$

• binary classification: $y = \{+1, -1\}$;

• multiclass classification: $y = \{1, 2,\cdots ,K \}$;

• regression: $y = \mathcal{R}$;

• structured learning: $y = $ structures;

• ......and a lot more!!

2、Learning with Different Data Label $y_n$

• supervised: known all labels $y_n$;

• unsupervised: unknown labels;

• semi-supervised: some labels known;

• reinforcement: implicit yn by goodness ($\tilde{y_n}$);

• ......and more!!

3、Learning with Different Protocol $f \Rightarrow (x_n; y_n)$

  Protocol $\Leftrightarrow$ Learning Philosophy

• batch: "duck feeding" (learn everything at the same time);

• online: "passive sequential" (每次学习一个样本);

• active: "question asking" (sequentially)

     —query the $y_n$ of the chosen $x_n$.

Active: improve hypothesis with fewer labels (hopefully) by asking questions strategically.

  除此之外，还有min-batch，即介于batch和online之间，每次选取一小部分数据进行学习。

4、Learning with Different Input Space $\mathcal{X}$

• concrete: sophisticated (and related) physical meaning;

• raw: simple physical meaning;

• abstract: no (or little) physical meaning;

• ......and more!!

concrete features: each dimension of $X\in  R^d$ represents "sophisticated physical meaning".

concrete features 是指能够反映当前机器学习任务间最本质区别或联系的特征。