Computer vision is one of the hottest research fields in the data science world. Moreover, it has become a part of our personal lives. Knowingly or unknowingly, we all use various features which have computer vision techniques running at the backend. For instance, we use the face unlock in our smartphones. The image below is efficiently explaining how does face detection works.

Reinforcement learning is a machine intelligence
scheme for learning in highly dynamic and probabilistic environments. The methodology, however, suffers
from a major drawback; the convergence to an optimal solution usually requires high computational expense since all states should be visited frequently in
order to guarantee a reliable policy

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer’s disease (AD). 

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.^[1] It infers a function from labeled training data consisting of a set of training examples.^[2] In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.