Day2,什么是机器学习？为什么用机器学习？ 机器学习有哪些分类？

机器学习（machine learning）的定义：

Machine Learning is the science (and art) of programming computers so they can
learn from data.
Here is a slightly more general definition:
[Machine Learning is the] field of study that gives computers the ability to learn
without being explicitly programmed.
—Arthur Samuel, 1959
And a more engineering-oriented one:
A computer program is said to learn from experience E with respect to some task T
and some performance measure P, if its performance on T, as measured by P, improves
with experience E.
—Tom Mitchell, 1997

For example, your spam filter is a Machine Learning program that can learn to flag spam given examples of spam emails (e.g., flagged by users) and examples of regular (nonspam, also called “ham”) emails. The examples that the system uses to learn are called the training set. Each training example is called a training instance (or sample). In this case, the task T is to flag spam for new emails, the experience E is the training data, and the performance measure P needs to be defined; for example, you can use the ratio of correctly classified emails. This particular performance measure is called accuracy and it is often used in classification tasks.

机器学习擅长的领域

Machine Learning is great for:
• Problems for which existing solutions require a lot of hand-tuning or long lists of rules: one Machine Learning algorithm can often simplify code and perform better.
• Complex problems for which there is no good solution at all using a traditionalapproach: the best Machine Learning techniques can find a solution.
• Fluctuating environments: a Machine Learning system can adapt to new data.
• Getting insights about complex problems and large amounts of data.

机器学习根据不同的标准可以进行如下划分：

• Whether or not they are trained with human supervision (supervised, unsupervised, semisupervised, and Reinforcement Learning)
• Whether or not they can learn incrementally on the fly (online versus batch learning)
• Whether they work by simply comparing new data points to known data points, or instead detect patterns in the training data and build a predictive model, much like scientists do (instance-based versus model-based learning)