Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets. Machine learning overlaps heavily with statistics, since both fields study the analysis of data. Unlike classical statistics, Machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard so part of machine learning research is the development of tractable approximate inference algorithms. Modern Machine Learning is is thus synonymous with 'computational statistics'.

Machine learning has a wide spectrum of applications including search engines, medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, game playing and robot locomotion.

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

Algorithm types

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

• supervised learning --- where 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 --- which models a set of inputs: labeled examples are not available.
• reinforcement learning --- where 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, tried to predict new outputs based on training inputs, training outputs, and new inputs.
• learning to learn --- where the algorithm learns its own inductive bias based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory.

Machine learning topics

This list represents the topics covered on a typical machine learning course.

• Modeling conditional probability density functions: regression and classification
  • Decision trees
  • Perceptron
  • Neural networks
  • Radial basis functions
  • Support vector machines
  • Gaussian process regression
• Modeling probability density functions through generative models:
  • Expectation-maximization algorithm
  • Bayesian networks also known as graphical models
  • Generative Topographic Mapping
• Appromixate inference techniques:
  • Markov chain Monte Carlo method
  • Variational methods
• Optimization: most of methods listed above either use optimization or are instances of optimization algorithms.

See also

• Artificial intelligence
• Computational intelligence
• Data mining
• Pattern recognition
• important publications in machine learning(computer science)
• Important publications in machine learning(statistics)
• Autonomous robot
• Inductive logic programming

Bibliography

• Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0198538642
• MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0521642981
• Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0070428077

External links

• UCI description
• Weka Machine Learning Software
• MLnet Mailing List
• Machine Learning and Natural Language Processing @ University of Freiburg
• Machine Learning and Data Mining in Bioinformatics Group @ TU München
• Machine Learning and Biological Computation Group @ University of Bristol
• Machine Learning and Applied Statistics @ Microsoft Research
• Journal of Machine Learning Research
• Machine Learning Journal
• Kmining List of machine learning, data mining and KDD scientific conferences
• Book "Intelligent Systems and their Societies" by Walter Fritz
• Links from Open Directory Project
• Machine Learning papers @ CiteSeer