机器学习基石（一）

台湾大学林轩天机器学习基石第一课：

When Can Machine Learn?
Why Can Machine Learn?
How Can Machine Learn?
How Can Machine Learn Better?

一、What is Machine Learning
Improving some performance measure with experence computed from data.


什么情况下会用机器学习来解决问题呢？
1.exists some ‘underlying pattern’ to be learned
—so ‘performance measure’ can be improved
2.but no programmable(easy) definition
—so ‘ML’ is needed
3. somehow there is data about the pattern
—so ML has some ‘inputs’ to learn from
二. 机器学习的应用领域(ML is everywhere)
food（食）
data: twitter data(words+location)
skill: tell food poisoning likeliness of restaurant properly
clothing（衣）
data: sales figures +clientsurveys
skill: give good fashion recommendations to clients
housing（住）
data: characteristics of buildings and their energy load
skill: predict energy load of other buildings closely
transportation（行）
data: some traffic sign images and meanings
skill: recognize traffic signs accurately
recommender system:（推荐系统）
data: how many users have rated some movies
skill: predict how a user would rate an unrated movie
例如：预测银行决定是否把信用卡给顾客

learning flow for credit approval

注意：
1. target f unknown
2. hypothesis g hopefully 约等于 f
but possibly different from f