Machine Learning

From today, I will start to study the machine learning course taught by Professor Andrew Ng. Here I will record my study notes and experience.

Week 1

Welcome

  • What’s machine learning?

There’s a science of getting computers to learn without being [explicitly programmed](https://intellipaat.com/community/7509/difference-between-machine-learning-and-explicit-programming#:~:text=Explicitly programmed%3A Writing out the,to you about Machine Learning.)（显式编程）%EF%BC%88%E6%98%BE%E5%BC%8F%E7%BC%96%E7%A8%8B%EF%BC%89)

Introduction

Machine Learning

  • Grew out of work in AI
  • New capability for computers

Examples

  • Database mining Large datasets from growth of automation/web E.g., Web click data(网络点击数据), medical records, biology, engineering
  • Applications can’t program by hand(不能通过手工编程实现) E.g., Autonomous helicopter, handwriting recognition(手写识别), most of Natural Language Processing(NLP)（自然语言处理）, Computer Vision(计算机视觉).
  • Self-customizing programs E.g., Amazon Netflix product recommendations
  • Understanding human learning(brain, real AI)

What is Machine Learning

Machine Learning definition

  • Arthur Samuel. Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
  • Tom Mitchell Well -posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Machine learning algorithms:

  • Supervised learning(监督学习)
  • Unsupervised learning(无监督学习)
  • Others: Reinforcement learning(强化学习), recommender systems(推荐系统).
  • Also talk about: Practical advice for applying learning algorithms

Supervised Learning

Eg1: Housing price prediction

“right answers” given

This is also called a regression problem

Regression: Predict continuous valued output(price)

Eg2: Breast cancer(malignant, begin)

  • Classification Discrete valued output(0 or 1)