Week 1 的内容主要有: 机器学习的定义 监督式学习和无监督式学习 线性回归和成本函数 梯度下降算法 线性代数回归 主要是了解一下机器学习的基本概念,重点是学习线性回归模型,以及对应的成本函数和梯度下降算法. 以上两幅图基本上就是week 1 的重点了. 下文是我做的比较粗糙的一个关于week 1 的总结.其中线性代数部分就省略掉了,大学里基本都有学过矩阵相关的操作.…

Machine Learning - Andrew Ng - Coursera Contents 1 Notes 1 Notes 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 exp…

Week1: Machine Learning: 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. Supervised Learning:We alr…

Week 1: Machine Learning: 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. Supervised Learning:We al…

1.监督学习(supervised learning)&非监督学习(unsupervised learning) 监督学习:处理具有若干属性且返回值不同的对象.分为回归型和分类型:回归型的返回值是连续的,分类型的返回值是离散的. 非监督学习:将具有若干属性的相同对象分为不同的群体. 2.线性回归模型(监督学习) 2.1 一些符号 m——训练样本数目 x——输入变量 y——输出变量 (x,y)——一个训练样本 (x(i),y(i))——第i个训练样本 h——假设(hypothesis)——预测函数…

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1.Feature Scaling(特征缩放): 如上图所示,x1是房屋面积,x2是房间个数,若不进行特征缩放,则代价函数J的曲线近似为一个瘦长的椭圆(我暂时这么理解,θ1和θ2分别是x1和x2的权值系数,而x2的特征向量值相较x1很小,则x1变化一个较小的量,在J的同一条相同的圆弧曲线上θ2就要变化一个较大的量,因此成为一个椭圆形式) 而对于左图的椭圆,会加大用梯度下降算法到达最低点的难度,所以我们可以采用右图的特征缩放,是他们都缩放到同一个数量级,这样J的形状近似为一个圆,更容易达到最低点.…

一.由线性回归导出逻辑回归: 二.“一对多”算法解决多分类问题: 三.“过拟合”和“欠拟合”: (1)对线性回归加入正则项: (2)对逻辑回归加入正则项: (3)加入正则项之后的正规方程:…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…