Machine learning: what and why?(摘录)

Machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data!).
Machine learning is usually divided into two main types. The first is the predictive or supervised learning approach,such as (xi,yi) and yi is called reponse value, when yi is categorical, the problem is known as classification or pattern recognition,and when yi is real-valued, the problem is known as regression. Which can be used in:
1.Classifying flowers.
2.Image classification and handwriting recognition.
3.Face detection and recognition.
4.Regression.
The second is unsupervised learning, where we are just given output data, without any inputs. The goal is to discover “interesting structure” in the data; this is sometimes called knowledge discovery. Labeled data is not only expensive to acquire6, but it also contains relatively little information, certainly not enough to reliably estimate the parameters of complex models. Which can be used in:
1.Discovering latent factors.
2.Discovering graph structure.
3.Discovering clusters.
4....
there is no universally best model — this is sometimes called the no free lunch theorem.