Machine learning

For the journal, see Machine Learning (journal). Machine Learning is a peer-reviewed Scientific journal, published since 1986

As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation At a general level, there are two types of learning: inductive, and deductive. Induction or inductive reasoning, sometimes called inductive logic, is the process of Reasoning in which the premises of an argument are believed Deductive reasoning is Reasoning which uses deductive Arguments to move from given statements ( Premises to Conclusions which must be true if the 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 theoretical computer science. Data mining is the process of Sorting through large amounts of data and picking out relevant information Statistics is a mathematical science pertaining to the collection analysis interpretation or explanation and presentation of Data. Theoretical computer science is the collection of topics of Computer science that focuses on the more abstract logical and mathematical aspects of Computing, such

1 Applications
2 Human interaction
3 Algorithm types
4 Machine learning topics
5 See also
6 References
7 Further reading
8 External links

Applications

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brain-machine interfaces and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion. Natural language processing ( NLP) is a subfield of Artificial intelligence and Computational linguistics. Syntactic pattern recognition or structural pattern recognition is a form of Pattern recognition, where items are presented pattern structures which can take into Diagnosis is the identification by Process of elimination, of the nature of anything Bioinformatics is the application of information technology to the field of molecular biology A brain-computer interface (BCI sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal Cheminformatics (also known as chemoinformatics and chemical informatics) is the use of computer and informational techniques applied to a range of problems Credit card fraud is a wide-ranging term for Theft and Fraud committed using a Credit card or any similar payment mechanism as a fraudulent source A stock market, or (equity market is a private or public market for the trading of company Stock and derivatives of company A DNA sequence or genetic sequence is a succession of letters representing the Primary structure of a real or hypothetical DNA Molecule Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to machine-readable input (for example to keypresses Handwriting recognition is the ability of a computer to receive and interpret intelligible Handwritten input such as pendrives digital cameras and other devices Object recognition in Computer vision is a task of finding given object in an image or video sequence Computer vision is the science and technology of machines that see A strategy game is a Game (eg computer, video or Board game) in which the players' decision-making skills have a high significance Robot locomotion is the study of how to design Robot appendages and control mechanisms to allow robots to move fluidly and efficiently

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system 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. Scientific method refers to bodies of Techniques for investigating phenomena

Some statistical machine learning researchers create methods within the framework of Bayesian statistics. Bayesian inference is Statistical inference in which evidence or observations are used to update or to newly infer the Probability that a hypothesis may be true

Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation Taxonomy is the practice and science of classification The word comes from the Greek, taxis (meaning 'order' 'arrangement' and, nomos Common algorithm types include:

Supervised learning — in which the algorithm generates a function that maps inputs to desired outputs. Supervised learning is a Machine learning technique for learning a function from training data 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 $[X_1, X_2, \ldots X_N]\,$ into one of several classes by looking at several input-output examples of the function. Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred
Unsupervised learning — An agent which models a set of inputs: labeled examples are not available. In Machine learning, unsupervised learning is a class of problems in which one seeks to determine how the data are organised
Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier. In Computer science, semi-supervised learning is a class of Machine learning techniques that make use of both labeled and unlabeled data for training - typically a
Reinforcement learning — in which the algorithm learns a policy of how to act given an observation of the world. Inspired by related psychological theory in Computer science, reinforcement learning is a sub-area of Machine learning concerned with how an agent 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. In Logic, Statistical inference, and Supervised learning, transduction or transductive inference is Reasoning fromobserved specific
Learning to learn — in which the algorithm learns its own inductive bias based on previous experience. Multi-task learning is an approach to Machine learning, that learns a problem together with other related problems at the same time using a shared representation The inductive bias of a learning Algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered (Mitchell 1980

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Theoretical computer science is the collection of topics of Computer science that focuses on the more abstract logical and mathematical aspects of Computing, such In Theoretical computer science, computational learning theory is a mathematical field related to the analysis of Machine learning algorithms