This is my blog.
ACM暂定后
打算找点事情做做的我
开始学习当下热门的机器学习了
先从吴恩达的课程开始的
这个笔记是在看课程的时候，记录下的。“好记性不如烂笔头”么，也方便之后的回顾。
小黄鸡给我推荐了计算机视觉，说是我这类人比较适合，打算听取一下啦！
Lesson 1 What is Machine learning
Examples：
- 数据挖掘
- 不可人工编写的程序（如手写识别
- 用户自定制化程序
Basic elements：
Task T，performance measure P，experices E
以下是四个机器学习的分类，其中前两个是重要的分类
*Supervised learning
- 回归问题 regression problem have continuous output
- Classification problem have discrete output
we are given a data set and already know what our correct output should look like and then predict the new question
Given the “right answer“ for each example in the data
Support vector machine use the infinite features to deal with the problem
some Notation:
training set — data set
m — number of training set
x’s — “input” variable / features
y’s — “output” variable / “target” variable
$(x^{(i)},y^{(i)})$ — training examples
h — hypothesis, it means $x\Rightarrow^h\Rightarrow y$
*Unsupervised learning
- Clustering problem 如给一堆数据分成n个类，n不确定
- Non-clustering problem 如将声音分成人声和背景或者多个人说话将每个人的声音区分开
with little or no idea what our results should look like
Reinforcement learning
Recommender systems
转载请注明出处，谢谢。
愿 我是你的小太阳
