【Machine Learning】监督学习、非监督学习及强化学习对比

• Supervised Learning
• Unsupervised Learning
• Reinforced Learning

Goal：

• How to apply these methods
• How to evaluate each methods

What is Machine Learning?

1.computational statistics
2.computational artifacts(人工制品) that learn over time based on experience

一、分类

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

1.1 Supervised learning——Approximation

• 一句话实质：About Function Approximation(函数逼近)，or Approximate function induction(近似函数归纳)
• feed with labeled examples，comeing up with some function that generalizes beyond(泛化函数)
• 有反馈

1.2 Unsupervised learning——Description

• 一句话实质：About Compact(简洁的) Description
• 无监督学习是密切相关的统计数据密度估计的问题。
• 无反馈
• Unsupervised learning could be helpful in the supervised Setting

1.3 Reinforcement learning (增强学习)

• 一句话实质：Learning from delayed reward (通过延迟性奖励进行学习)
• 执行许多步之后才知道反馈，就像下棋（对比监督学习的立即反馈）

二、归纳法(induction)与演绎法(deduction)

• Generalize 泛化
• 了解机器学习发展史
• 机器学习算法与归纳而不是演绎有关
• Inductive bias 归纳偏差

归纳：从示例到一般规律（从一个示例得出更普遍的规律）

演绎：从规则到实例，a general rule to specific instances,basically like reasoning(推理)

三、三种机器学习的比较

表述成：优化问题

Supervised Learning —— labels data well(to find a funtion to score that) （标记数据）
Unsupervised Learning —— cluster scores well(最好的分类方法)
Reinforcement learning —— behavior scores well (最好的表现)

3.2 Data

Data is king in machine learning.

转变：以算法为中心——》以数据为中心

• Believe in your data！