林轩田-机器学习基石 课堂笔记（一）A takes D and H to get g

1.From Learning to Machine Learning

Learning: Observations->learning->skill

Machine Learning: data->ML->skill

ps: skill — improve some performance measure (eg: prediction accuracy)

2.Key Essence of ML:(help decide whether to use ML)

  ①exists some ‘underlying pattern’ to be learned

      —— so ‘performance measure’ can be improved

  ②but no programmable (easy) definition

      —— so ML is needed

  ③somehow there is data about the pattern

      —— so ML has some ‘input’ to learn from

3.The Learning Model（※）

A more appropriate definition of ML: use data to compute hypothesis g target f .

NOTE：课中以银行是否会同意用户申请办理信用卡为例

1>收集的（单个）用户信息包含的内容，可理解为输入内容：

2>基本的符号含义以及其对应本例的含义：

3>假设函数集

从图中我们可以知道机器学习从由大量数据分析中得到假设函数集，从而得到一个能近似表示输入输出集映射关系的目标函数g，通过g来进行预测，

更通俗的来讲，机器学习是人工智能的一个分支，我们使用计算机设计一个系统，使他能根据提供的训练数据按照一定的方法来学习，随着训练数据次数的增加，该系统可以在性能上不断学习和改进，通过参数优化和学习模型，能够用于预测相关问题的输出。

4.ML vs DM/AI/Statistic

  ①Machine Learning: use data to compute hypothesis g that approximates target f

  ②Data Mining: use (huge) data to find property that is interesting

     PS: a. if ‘interesting property’ same as ‘hypothesis that approximate target’,

               ML=DM

            b. if ‘interesting property’ related to ‘hypothesis that approximate target’,

               DM can help ML (often, but not always)

  ③Artificial Intelligence: compute something that shows intelligent behavior

     PS: g ≈ f is something that shows intelligent behavior

           —— ML can realize AI, among other route

           e.g.: chess playing

                     traditional AI: game tree

                     ML for AI: ‘learning from board data’

  ④use data to make inference about an unknown process

     PS: g is a inference outcome;

           f is something unknown.

           —— Statistic can be used to achieve ML, traditional Statistic also focus on

        provable results with math assumptions, and care less about computation.