is a subfield of artificial intelligence
that is concerned with the design and development of algorithms
and techniques that allow computers to "learn". In general, there are two types of learning: inductive
, and deductive
. Inductive machine learning methods extract rules and patterns out of massive data sets.
The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also to theoretical computer science.
Applications for machine learning include natural language processing
, syntactic pattern recognition
, search engines
, medical diagnosis
, brain-machine interfaces
, detecting credit card fraud
, stock market
analysis, classifying DNA sequences
and handwriting recognition
, object recognition
in computer vision
, game playing
and robot locomotion
Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. But human intuition cannot be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method
Some statistical machine learning researchers create methods within the framework of Bayesian statistics.
Machine learning algorithms
are organized into a taxonomy
, based on the desired outcome of the algorithm. Common algorithm types include:
- Supervised learning — in which the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate) the behavior of a function which maps a vector into one of several classes by looking at several input-output examples of the function.
- Unsupervised learning — An agent which models a set of inputs: labelled examples are not available.
- Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
- Reinforcement learning — in which the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
- Transduction — similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
- Learning to learn — in which the algorithm learns its own inductive bias based on previous experience.
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science
known as computational learning theory
Because training sets are finite and the future is uncertain, learning theory usually does not yield absolute guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common.
In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
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