1.1 What is Machine Learning?

什么是机器学习？

Two definitions of Machine Learning are offered.
有两个机器学习的定义：

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.
Arthur Samuel 定义为：机器学习在无特定编程程序情况下给予计算机学习的能力。这是旧的，非正式的定义。

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.”
Tom Mitchell 对机器学习的定义为：计算机程序从 经验 E 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

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:
通常，任何种类的机器学习可以分为如下两类

  • Supervised learning

监督学习

  • Unsupervised learning.

无监督学习

1.2 Supervised learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
在监督学习中，我们得到一个数据集并且已知正确的输出，并且认为输入和输出之间存在关系

Supervised learning problems are categorized into “regression” and “classification” problems.
监督学习问题被分类为“回归”和“分类”两类问题。

In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
在回归问题中，我们试图预测连续输入的结果，意味着我们试图将输入变量映射到某个连续函数上。

In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
在分类问题中，相反，我们试图预测离散输出的结果。 换句话说，我们正在尝试将输入变量映射为离散变量。

Example 1:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
给定有关房地产市场上房屋大小的数据，请尝试预测其价格。 价格作为规模的函数是一个连续的输出，因此这是一个回归问题。

We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.
我们可以通过输出房子“卖得比要价高还是低”，把这个例子变成一个分类问题。这里我们根据价格将房屋分为两类。

Example 2:
(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture
回归-给定一个人的照片，我们必须根据给定的照片来预测他们的年龄

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
分类-对于患有肿瘤的患者，我们必须预测肿瘤是恶性还是良性的

1.3 Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
无监督学习使我们可以在几乎或者根本不知道结果的情况下如何处理问题。我们可以从数据中获取结构，而不必要知道变量的影响。

We can derive this structure by clustering the data based on relationships among the variables in the data.
我们可以通过根据数据中变量之间的关系对数据进行聚类得到这种结构。

With unsupervised learning there is no feedback based on the prediction results.
无监督学习没有基于预测结果的反馈。

Example:
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
聚类：收集1,000,000个不同的基因，并找到一种方法将这些基因自动分组为 变量在某种程度上相似或相关的 组（例如寿命，位置，角色等）

Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
非集群：“鸡尾酒会算法”，可以在混乱的环境中找到结构。 （即在鸡尾酒会上从一系列声音中识别出个人声音和音乐）。