today I want to make a short notice on question that I often receive: what kinds of learning exists in Machine Learning. I want to provide simple answer:
- Learning with teacher: questions and answers.
- Learning with teacher: just questions
- Partial learning: questions, and some of them with answers
- Active learning: at which object you'll get an answer
I suppose maybe some variations of this also exists, but as usually ML in one or another way manipulates with those four.
Consider example of clusterization. You have some data set of values. And you need somehow to group them or in other words find groups of similar objects. This task has two problems: nobody know number of groups. And second one we don't know real clusters. Real clusters that we want to distinguish. That provides the challenge that you by yourself can't evaluate answer from ML algorithm. While reading this example you may wonder who may need such task? I give you few examples:
- Segmentation of users for mobile network operator or for some e-shop
- Search for similar users in social networks
- Serch in genome for similar profiles of expression
Second example can be task of visualization of some group. Imagine that you have some set of data, and want not just to know how much groups you have, but want to have some visual representation of it.
Third example is search for anomalies. Consider the following scenario. You have successfull web site that has many visitors: tens of thousands. And among them you can have 1 - 2 hackers. Or don't have hackers at all. How to detect them? This is also learning without teacher.