coursera Machine learning Andrew NG 笔记(一)
来源:互联网 发布:windows徽标键与t 编辑:程序博客网 时间:2024/05/18 02:06
看到不少推荐Andrew Ng的机器学习的课程,所以在coursera上注册了开始学。2016年1月15日
1. Introduction
1. machine learning definition
Arthur Samuel(1959): Machine learning is a field of study that gives computers the ability to learn without explicitly programmed.
Tom Mitchell(1998)(CMU) :Well-posed learning problem:A computer program is said to learn from experience E with respect to some task T, and some performance measure P, if its performance T, as measured by P, improves with exprience E.
2. Main two types of machine learning
(1)supervised learning(监督学习)
(2)unsupervised learning(非监督学习)
其他还有reinforcement learning(比如check playing),recommender system
3. Supervised learning
(1) refers to the fact that we gave the algorithm a data set in which “right answers” were given.
(2)主要分为regression(回归,主要output 为连续的)和 classification(分类,output是离散的)问题,regression 可以为线性,可以为非线性。
(3) SVM(support vector machine,支持向量机)
4. Unsupervised learning
(1) 没有标签(label),没有告诉right answer
(2) Clustering problem(聚类问题)
E.g Google news group automatically cluster news stories into groups about the same topic.
(3) Applications: Genes; organizing large computing clusters; social network analysis; market segmentation; astronomical data analysis
(4)Cocktail party problem
分离两个不同来源但叠加在一起的声音input1和input2
svd: single value decomposition
2. Linear regression with one variable
Cost Function
1. Model Representation
traning set
m = # of traning examples
(x, y) a single traning example
()
2. Cost Function
- Fit the best line to training examples
- 线性回归 Hypothesis:
hθ (x )=θ0 +θ1 x1 - idea: 将x带入
hθ (x )得到estimated y,
因此minimize J(θ0 ,θ1 ) =12m ∑i=1m(hθ(x(i))−y(i))2
cost function= J(
3. Gradient Descent
- gradient descent是解决线性回归问题的一种方法,不断重复以下步骤,使得J逐渐收敛至最小值
θj:=θj−αδδθjJ(θ0,θ1)
每一步θ0,θ1 都是同时update - 需要注意的问题
选择合适大小的α - 对于超过两个变量的cost function同样适用
可以归纳为
repeat{
θj:=θj−α1m ∑i=1m(hθ(x(i))−y(i))x(i)j }
注意也是同时更新的 - 技巧
(1)feature scaling
(2)Mean normalization
(3)怎么判断gradient descent是适用的?
plots can be helpful
4. Linear regression and normal equations
对每一个
set
solve for
可以推导出
5. Normal equation和gradient descent 的对比
当变量很多时,如
但n较小时,需要选择
- coursera Machine learning Andrew NG 笔记(一)
- coursera Machine Learning, Andrew Ng
- Andrew Ng -machine learning 课堂笔记(一)第六周
- coursera andrew Ng老师的machine learning的课程总结(一)
- coursera Machine learning Andrew NG 学习笔记(二)—Logistic regression
- Andrew Coursera Machine Learning
- Andrew Ng's Machine Learning in Coursera(I)
- Machine Learning - Andrew Ng on Coursera (Week 1)
- Machine Learning - Andrew Ng on Coursera (Week 2)
- Machine Learning - Andrew Ng on Coursera (Week 3)
- Machine Learning - Andrew Ng on Coursera (Week 4)
- Machine Learning - Andrew Ng on Coursera (Week 5)
- Machine Learning - Andrew Ng on Coursera (Week 6)
- Outline of Machine Learning created by Andrew Ng on Coursera
- Coursera—machine learning(Andrew Ng)第二周编程作业
- Coursera—machine learning(Andrew Ng)第三周编程作业
- Coursera—machine learning(Andrew Ng)第四周编程作业
- Coursera—machine learning(Andrew Ng)第五周编程作业
- 关于ViewFlipper的使用
- 使用Spock框架进行单元测试
- leveldb代码阅读(15)——内存中的数据结构Memtable/SkipList
- Redis数据备份与恢复
- 字典转为Json字符串
- coursera Machine learning Andrew NG 笔记(一)
- 《偏向锁,轻量级锁,重量级锁》优缺点对比
- BasicNameValuePair的简单使用
- 以下是unix的命令行,供参考 ios
- php简易增删改查
- 关于web.py使用模版传值问题
- 数据库范式
- iOS开发UI篇—核心动画(转场动画和组动画)
- jquary实现全选功能