01 The Learning Problem

本系列文章为林軒田老师機器學習基石上课程学习笔记，见详细课件。

课程主线

• When Can Machines Learn? (illustrative + technical)
• Why Can Machines Learn? (theoretical + illustrative)
• How Can Machines Learn? (technical + practical)
• How Can Machines Learn Better? (practical + theoretical)

也就是要依次回答：何时可以用机器学习？为何可以机器学习？怎样机器学习？怎样更好地机器学习？构建一幅大Picture！

机器学习应用场景

首先有机器学习不同侧面的定义：

• Improving some performance measure with experience computed from data
• Use data to compute hypothesis

g

that approximates target

f

Key Essence of Machine Learning:

• A pattern exists(比如随机数生成不可学习)
• We cannot pin it down mathematically(否则直接公式表示)
• We have data on it

思考机器学习的这三个key essence，界定遇到的问题是否可用机器学习方法解决。

以下是一些典型的应用场景：

• When human cannot program the system manually, like navigating on Mars
• When human cannot ‘define the solution’ easily, like speech/visual recognition
• When needing rapid decisions that humans cannot do, like high-frequency trading
• When needing to be user-oriented in a massive scale, like consumer-targeted marketing

问题的Formulation

首先明确其中五个元素：

• 定义input space

x \in X

• 定义output space

y \in Y

• Target function: unknown pattern to be learned
• Training examples:

D={(x1,y1), (x2,y2),\dots, (xN,yN)}

• Hypothesis: skill with hopefully good performance

最终机器学习Formulation为：

利用target function生成的training examples数据，通过learning algorithm从hypothesis set里找出

g

使其尽可能接近target function

f

.

从上面可以看出一个假设，就是训练数据集

D

是从target function来的，为保证学习效果，

D

需要足够representative。