机器学习1
Introduction
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 explicitly programmed.” This is an older, informal definition.
Tom Mitchell provides a more modern definition:
“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(玩的经验)
T = the task of playing checkers.(要做的事情)
P = the probability that the program will win the next game.(事情做成的机率)Supervised Learning
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.
Supervised learning problems are categorized into “regression” and “classification” problems. 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. 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.
Example:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.
Unsupervised Learning
Unsupervised learning, on the other hand, 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.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It’s not just about clustering. For example, associative memory is unsupervised learning.
Example:
Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.
Associative: Suppose a doctor over years of experience forms associations in his mind between patient characteristics and illnesses that they have. If a new patient shows up then based on this patient’s characteristics such as symptoms, family medical history, physical attributes, mental outlook, etc the doctor associates possible illness or illnesses based on what the doctor has seen before with similar patients. This is not the same as rule based reasoning as in expert systems. In this case we would like to estimate a mapping function from patient characteristics into illnesses.
一元线性回归
最为主要的一些公式
一元线性函数
花费函数，也就是用来逼近我们需要的俩个参数的函数
目的函数，所以对他求导
梯度下降法求解目的函数
从一个猜测的初始值出发，不断的减小目的函数
梯度下降算法
具体实现
第一周顺利结束
感觉还不错，因为原来是看过的，但是还是有一点需要注意的。
- 梯度下降参数α这个值因为考虑到下降速度的问题还是需要变化的，但是会有不够和过了的问题。
还有如下的一些问题：
补
- 可以找到一个完全匹配的函数。
- 即使找到一个完全匹配的函数，但是不可以说是100%预测成功。