Approaches
General:
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
- Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.
Others:
- Semi-supervised learning
- Dimensionality Reduction
- Feature learning
- Sparse dictionary learning
- Anomaly detection
- Robot learning
- Association rules
Models
- Artificial neural networks
- Decision trees
- Support-vector machines
- Regression analysis
- Bayesian networks
- Genetic algorithms
- Training models
- Federated learning