Machine Learning definition

- Arthur Samuel(1959). Machine Learing :Field of study that gives computers the ability to learn without being explicitly programmed.

在进行特定编程的情况下赋予计算机学习能力的领域

- Tom Mitchell (1998)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.

一个程序被认为能从经验中学习，解决任务T，达到性能度量值P，当且仅当有了经验E后，经过P评判，程序在处理T时的性能有所提升。

Machine learing algorithms:

• Supervised learning
• Unsupervised learning
• Others:Reinforcement learning, recommender systems.
  Also talk about:Practical advice for applying learning algorithms.
• 监督学习
• 非监督学习
• 其他：强化学习，推荐系统
  接下来的主要任务是：了解应用学习算法的实用建议。

Examples:

• Database mining
  Large datasets from growth of automation/web.
  E.g., Web click data, medical records, biology, eng
  ineering
• Applications can’t program by hand.
  E.g., Autonomous helicopter, handwriting recognitio
  n, most of
  Natural Language Processing (NLP), Computer Vision.
• Self-customizing programs
  E.g., Amazon, Netflix product recommendations
• Understanding human learning (brain, real AI).

以上摘自 Andrew Ng。
第一节课了解到机器学习的定义，课程主要是讲算法的选择与应用。其中老师提到接下来的时间将会花费大量时间
在机器学习、人工智能的最佳实践以及如何让他们工作上，我们该如何去做，也就是学习并学会使用这些算法。