课程记录笔记如下: 1.目前ML的应用 包括:数据挖掘database mining.邮件过滤email anti-spam.机器人autonomous robotics.计算生物学computational biology.搜索引擎Google/Bing. 自动直升机autonomous helicopter.自然语言处理Natural Language Processing 2.ML的定义 3.目前ML的分类 监督学习Supervised Learning.无监督学习Unsupervised…

<Machine Learning - 李宏毅> 学习笔记 b站视频地址:李宏毅2019国语 第一章 机器学习介绍 Hand crafted rules Machine learning ≈ looking for a function from data Speech recognition Image recognition Playing go Dialogue system Framework define a set of function goodness of function…

此文是斯坦福大学,机器学习界 superstar - Andrew Ng 所开设的 Coursera 课程:Machine Learning 的课程笔记. 力求简洁,仅代表本人观点,不足之处希望大家探讨. 课程网址:https://www.coursera.org/learn/machine-learning/home/welcome Week 3: Logistic Regression & Regularization 笔记:http://blog.csdn.net/ironyoung/ar…

此文是斯坦福大学,机器学习界 superstar - Andrew Ng 所开设的 Coursera 课程:Machine Learning 的课程笔记. 力求简洁,仅代表本人观点,不足之处希望大家探讨. 课程网址:https://www.coursera.org/learn/machine-learning/home/welcome Week 2:Linear Regression with Multiple Variables笔记:http://blog.csdn.net/ironyoung…

这是Coursera上比较火的一门机器学习课程,主讲教师为Andrew Ng.在自己看神经网络的过程中也的确发现自己有基础不牢.一些基本概念没搞清楚的问题,因此想借这门课程来个查漏补缺.目前的计划是先看到神经网络结束,后面的就不一定看了. 当然,看的过程中还是要做笔记做作业的,否则看了也是走马观花.此笔记只针对我个人,因此不会把已经会了的内容复述一遍,相当于是写给自己的一份笔记吧.如果有兴趣,可以移步<Machine Learning>仔细学习. 接下来是第一周的一些我认为需要格外注意的问题.…

##Linear Regression with One Variable Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradi…

##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…

1. 机器学习的定义:Machine learning is programming computers to optimize a performance criterion(优化性能标准) using example data or past experience. 2. 监督学习(Supervised Learning):The term supervised learning refers to the fact that we gave the algorithm a data set…

About this Course Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly i…

1 特征 1-1 什么是特征? 我的理解就是,用于描述某个样本点,以哪几个指标来评定,这些个指标就是特征.比方说对于一只鸟,我们评定的指标就可以是:(a)鸟的翅膀大还是小?(b)鸟喙长还是短?(c)鸟下的蛋是多还是少?等等,这些都能被称之为“鸟”这个样本点的特征. 特征值的数量用“n”来表示.比如我们用一些特征来描述一栋房子,这些特征包括:(a)多少平米?(b)几室几厅?(c)有几层?(d)房子是新还是旧?那么这里就有4个特征,也就是n=4. 1-2 现在我们区分一下符号 (1)m:样本点的数目…