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If an expert system--brilliantly designed, engineered and implemented--cannot learn not to repeat its mistakes, it is not as intelligent as a worm or a sea anemone or a kitten.
-Oliver G. Selfridge, from The Gardens of Learning.

"Find a bug in a program, and fix it, and the program will work today. Show the program how to find and fix a bug, and the program will work forever."
- Oliver G. Selfridge, in AI's Greatest Trends and Controversies, Marti A. Hearst and Haym Hirsh, Editors. IEEE Intelligent Systems (January/February 2000). A timely and thought provoking collection of views from AI scholars and practitioners.

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables.
A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Artificial intelligence is a closely related field, as are probability theory and statistics, data mining, pattern recognition, adaptive control, computational neuroscience and theoretical computer science.
- from wiki wiki

• Kernel machines
  A central information source for the area of Support Vector Machines, Gaussian Process prediction, Mathematical Programming with Kernels, Regularization Networks, Reproducing Kernel Hilbert Spaces, and related methods. Provides links to papers, upcoming events, datasets, code.
• Computational Learning Theory
  A research field devoted to studying the design and analysis of algorithms for making predictions about the future based on past experiences. The emphasis in COLT is on rigorous mathematical analysis. COLT is largely concerned with computational and data efficiency.
• Machine Learning in Games
  How computers can learn to get better at playing games. This site is for artificial intelligence researchers and intrepid game programmers. I describe game programs and their workings; they rely on heuristic search algorithms, neural networks, genetic algorithms, temporal differences, and other methods.
• Reinforcement Learning Repository
  A centralized resource for researchers of reinforcement learning. Maintained at University of Massachusetts, Amherst.
• Pattern Recognition on The Web
  Links to various pattern recognition and machine learning resources.
• Grammatical Inference
  Repository of information on grammatical inference, automata induction, and language acquisition.
• David W. Aha: Machine Learning Page
  A machine learning resources from Applications to Tutorials.
• Gowachin
  A competition on Grammatical Inference.
• Machine learning for user modeling
  Resources for researchers and practitioners interested in the use of learning techniques in intelligent, user-adaptive systems.
• Integrated Optimization - Artificial Intelligence
  Site dedicated to research of artificial intelligence algorithms applied to information retrieval, data mining and optimization methods. Includes FAQs and AI resources for math/science teachers and students.
• k-means clustering tutorial
  Introduction to k-means clustering, a popular data mining and unsupervised learning algorithm. Free code, software, resources and examples are available for download.
• Proto-Mind Machines
  Artificial Neural Network-based natural language conversational agent and intelligent dialogue generator.
• Programming by Example
  Programming by example (or by demonstration) is a technique for teaching the computer new behavior by demonstrating actions on concrete examples. The system records user actions and generalizes a program that can be used in new examples.
• Pattern Recognition Information
  A hub for Pattern Recognition linking to journals, books, bibliographies, jobs, conferences and news.
• Reasoning about Computational Resource Allocation
  An introduction to "anytime" algorithms. Published in Crossroads, the student magazine of the ACM.
• Andrew Schein's Web Page
  Machine learning approaches to data mining focussing on text mining applications.

• Data Mining on the Web
  Article by Dan Greening on data mining techniques applied to analyzing and making decisions from web data.
• Data Mining and Knowledge Discovery
  A peer-reviewed journal publishing articles on all aspects of Knowledge Discovery in Databases (KDD) and data mining methods for extracting high-level representations (patterns and models) from data. Accepts submissions of original research or technical survey articles of related fields and techniques.
• Distribution Analysis module for PostgreSQL -
  Graphical parameter distribution and function relations analysis software for PostgreSQL.
• About.com on Data Mining
  About.com presents a collection of original feature articles, net links, forum discussions and a chat room dedicated to data mining and data warehousing topics.
• Estimating Campaign Benefits and Modeling Lift (Overheads)
  In assessing the potential of data mining based marketing campaigns one needs to estimate the payoff of applying modeling to the problem of predicting behavior of some target population. We present a methodology for initial cost/benefit analysis and present surprising empirical results, based on actual business data from several domains, on achievable model accuracy.
• Digging Up Dollars with Data Mining - An Executive's Guide
  Tim Graettinger. Data mining creates information assets that an organization can leverage to achieve these strategic objectives. In this article, we address some of the key questions executives have about data mining.
• Web-Datamining
  Web-datamining.net gather information and exchanges on Data Mining, Statistics and Knowledge Discovery, including publications, meetings and tools. In French and English.
• Kurt Thearling: Data Mining and CRM
  Information on data mining and CRM technology. Includes a list of reference books, together with articles and white papers.
• The Data Mine
  Launched in April 1994 to provide information about Data Mining (AKA Knowledge Discovery In Databases or KDD). A Twiki site full of guides, info, and links.