Coursera斯坦福大学机器学习（Machine Leaning）课程第一周课程笔记
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
“对于某类任务T和性能度量P，如果一个计算机程序在T上以P衡量的性能随着经验E而自我完善，那么我们称这个计算机程序在从经验E学习。”
机器学习定义
机器学习有下面几种定义：
- 机器学习是一门人工智能的科学，该领域的主要研究对象是人工智能，特别是如何在经验学习中改善具体算法的性能。
- 机器学习是对能通过经验自动改进的计算机算法的研究。
- 机器学习是用数据或以往的经验，以此优化计算机程序的性能标准。
What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.
Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications: Supervised learning and Unsupervised learning.
监督学习
监督学习从给定的训练数据集中学习出一个函数，当新的数据到来时，可以根据这个函数预测结果。
监督学习的训练集要求是包括输入和输出，也可以说是特征和目标。训练集中的目标是由人标注的。
常见的监督学习算法包括回归分析和统计分类。
回归分析（连续）
房价预测
统计分类（离散）
癌症良恶性判断
分类（Classification）和回归（Regression）的区别在于输出变量的类型。
定量输出称为回归，或者说是连续变量（continous）预测；
定性输出称为分类，或者说是离散变量（discrete）预测。
非监督学习
We can derive this structure by clustering the data based on relationships among
the variables in the data.
应用场景：
- 市场细分
- 组织计算集群
- 社交网络分析
- 天文数据分析
无监督学习与监督学习相比，训练集没有人为标注的结果。常见的无监督学习算法有聚类。
聚类问题
基因数据分组
非聚类问题
鸡尾酒会问题（音频分离）