2019.6.22 Coursera Machine Learning 第一周课程笔记

最新推荐文章于 2023-12-25 00:53:02 发布

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最新推荐文章于 2023-12-25 00:53:02 发布

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分类专栏：机器学习 queena的学习笔记文章标签：机器学习

本文链接：https://blog.youkuaiyun.com/qq_31194443/article/details/93349279

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queena的学习笔记同时被 2 个专栏收录

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总结：

1.什么是机器学习？

"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.

https://mp.weixin.qq.com/s?__biz=MzIxMjE5MTE1Nw==&mid=2653190076&idx=1&sn=5ad9aab376fe9d6aa8d4092c1976aec0&chksm=8c990566bbee8c70fc9d354082cdc5ef4cf6a44dc3f448808f6b986de26b280fe8bdaae5a1f6&scene=21#wechat_redirect

2.监督学习

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.
（数据集已经知道输出、找输入和输出之间的关系）

Supervised learning problems are categorized into "regression" and "classification" problems. 
（有监督学习分为回归和分类问题）
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.
（连续值）
 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.
（离散值）

3. 无监督学习

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.
（只有一个数据集（无区别的数据集）、能在其中找到某种结构吗？）

We can derive this structure by clustering the data based on relationships among the variables in the data.
（对数据进行聚类，从而得出这种结构。常用聚类算法）

With unsupervised learning there is no feedback based on the prediction results.
(在无监督学习中，没有基于预测结果的反馈???)