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Intro to Machine Learning

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Intro to Machine Learning

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Machine learning is sub set of artificial intelligence and it is study of systems that can learn from data. A machine learning system could be trained. Core of machine learning deals with representation and generalization.

Machine learning is a "Field of study that gives computers the ability to learn without being explicitly programmed". A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set.

What is the different of machine learning and data mining?

  • Machine learning focuses on prediction, based on known properties learned from the training data.
  • Data mining focuses on the discovery of (previously) unknown properties in the data. (This is the analysis step of Knowledge Discovery in Databases)
  • Data mining uses many machine learning methods, but often with a slightly different goals
  • Machine learning also used data mining methods as "unsupervised learning" to improve learner accuracy

Algorithm types

  • Supervised learning (labelled)
  • Unsupervised learning (unlabelled)
  • Semi-supervised learning
  • Transduction (reasoning from observed)
  • Learning to learn (multi-task learning)
  • Reinforcement learning
  • Developmental learning (imitation)

Applications for machine learning

  • Computer vision (object recognition)
  • Natural language processing
  • Syntactic pattern recognition
  • Search engines
  • Medical diagnosis
  • Detecting credit card fraud
  • Stock market analysis
  • Speech and handwriting recognition
  • Game playing
  • Software engineering
  • Adaptive websites
  • Computational advertising
  • Computational finance

[1] http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/

[2] http://inside-bigdata.com/2014/04/18/quantum-machine-learning/

 

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