Learning is a wide domain and thus, the field of machine learning has branched into several sub fields dealing with different types of learning tasks.

 


Supervised vs Unsupervised Learning


Since learning involves an interaction between the learner and the environment, we can divide tasks according to the nature of the interaction. Consider the task of detecting the hand written numbers among the lots of digital numbers vs task of anomaly detection.

For the hand-written numbers detection task, we consider a setting in which the learner receives a set of training numbers, labeled as ‘Hand-Written’ and ‘Digital’. On the basis of such training, the learner should figure out a rule for labeling a newly arriving number.

In contrast, for the task of anomaly detection, all the learner gets, as a training is a large set of numbers, without any labels on it, and the learner’s task is to detect ‘unusual’ numbers.


Consider learning as a process of ‘using the experience to gain expertise’. Supervised learning describes a scenario in which the ‘experience’, a training example, consist significant information (labels Hand-Written or Digital) that is missing in the future arriving numbers, to which the learned experience is to be applied. Here, the acquired expertise is aimed at predicting the missing information for the validation data.


However, in unsupervised learning, there is no difference between the training data and validation data. The learner processes input data with the goal of coming up with some summary or compressed version of the data. Clustering a data set into subsets of similar objects is a good example of such task.


There is also an intermediate learning setting in which, the training set contains more information than the validation set. The learner here is required to predict even more information for the validation set. Consider a game of chess, where a value function describes each setting of a chess board. Now one may want to learn the degree why which White’s position is better than the black’s. The only information available to the learner at the training time is positions that occurred in an actual chess game and who won it. Such learning frameworks are investigated under reinforcement learning.



Source: ‘Understanding machine learning: From theory to algorithms’ written by Shai Shalev-Shwartz and Shai Ben-David