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Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and theoretical computer science.

Contents 1 Applications 2 Human interaction 3 Algorithm types 4 Theory 5 See also 6 Further reading 7 External links

[edit] Applications

Applications for machine learning include machine perception, computer vision, 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, software engineering, adaptive websites and robot locomotion.

[edit] Human interaction

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. Human intuition cannot, however, 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^{[citation needed]}.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

[edit] Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm.

Common algorithm types include:

• Supervised learning - Generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function.
• Unsupervised learning - Models a set of inputs: like clustering
• Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.
• Reinforcement learning - Learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm.
• Transduction - Tries to predict new outputs based on training inputs, training outputs, and test inputs.
• Learning to learn - Learns its own inductive bias based on previous experience.
• Pareto-based multi-objective learning - a Pareto-based approach to learning that results in a set of learning models, which typically trade off between performance and complexity. See Multi-Objective Machine Learning Website

[edit] Theory

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.

There are many similarities between Machine Learning theory and Statistics, although they use different terms.

[edit] See also

• Artificial Neural Network
• Autonomous robot
• Compound term processing
• Computational intelligence
• Data mining
• Explanation-Based Learning
• Fuzzy logic
• Genetic programming
• Important publications in machine learning (computer science)
• International Conference on Machine Learning (ICML) (conference)
• Inductive logic programming
• Intelligent system
• Journal of Machine Learning Research
• List of numerical analysis software
• Machine learning algorithms
• MLMTA Machine Learning: Models, Technologies & Applications
• Multi-label classification
• Neural Information Processing Systems (NIPS) (conference)
• Neural network software
• Pattern recognition
• Predictive analytics
• RapidMiner Open-source machine learning framework for pattern classification, regression, and clustering
• Reservoir computing
• WEKA Open-source machine learning framework for pattern classification, regression, and clustering.

[edit] Further reading

[edit] External links

• Ruby implementations of several machine learning algorithms
• Andrew Ng's Stanford lectures and course materials
• The Encyclopedia of Computational Intelligence
• International Machine Learning Society
• Kmining List of machine learning, data mining and KDD scientific conferences
• Machine Learning Open Source Software
• Machine Learning Video Lectures
• The Computational Intelligence and Machine Learning Virtual Community
• R Machine Learning Task View