- 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！
