Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Machine learning tasks are typically classified into two broad categories, depending on Whether there is a learning "signal" or "feedback" available to a learning system: 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. As special cases, the input signal can be only partially available, or restricted to special feedback. 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 (discovering hidden patterns in data) or a means towards an end (feature learning). Semi-Supervised Learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active Learning: The computer can only obtain training labels for a limited set of instances (based on a budget) and must optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement Learning: Training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.
- Find-S Algorithm
- Candidate Elimination Algorithm
- Decision Tree Algorithm
- Back Propagation Algorithm
- Naïve Bayes Algorithm
- K-Nearest Neighbour algorithm (Lazy Learning Algorithm)
- EM Algorithm
- K-Means Algorithm
- Locally Weighted Regression Algorithm