Machine Learning Algorithms fall roughly into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
the algorithm learns a function from given pairs of input and output (labeled data). In other words, it generates a function that maps inputs to desired outputs.
The algorithm generates a model for a set of inputs (unlabeled data). The main problem of this kind of learning is partitioning the input set into subsets in such way that each subset can be handled with an appropriate function. One example of using unsupervised learning is data compression, where the probability distribution of the input set plays an important role.
In Reinforcement Learning the aim is the maximization of rewards given as feedback on the actions which are performed in an environment by the learning agent. In other words: "Reinforcement learning is learning what to do--how to map situations to actions--so as to maximize a numerical reward signal."
|Modeling Approach||Regression and classification||Clustering||Markov Decision Process|
|popular algorithms||Linear Regression, Support Vector Machines (SVM), Neural Networks, Decision Trees, Naive Bayes, Nearest Neighbor.||k-means clustering, Association rule||Q-Learning, Deep Adversarial Networks|
|Applications||Predicting Modelling||Descriptive Modelling||Robotics, Self-driving cars|