Tuesday, May 13, 2014

Subfields Of Sciences As Inspiration For Machine Learning Algorithms/Paradigms

  • Perceptrons and Neural Networks were inspired by Models of Neuron of the brain. So Neuroscience is obviously a major inspiration.
  • Genetic Algorithms, Genetic Programming, Evolutionary Algorithms are inspired by Genetics and Evolutionary Theory.
  • Simulated annealing [1] Algorithm was invented for solving problems in Statistical Physics and later used in Optimization problems in Artificial Intelligence and Machine Learning.
  • Reinforcement Learning was first studied in Psychology, more specifically in Behavioral Psychology. Now Reinforcement Learning is a branch of Machine Learning. 
  • Statistics is the field most closely tied with Machine Learning apart from Computer Science. Many Regression and Clustering techniques from Statistics act as inspiration to Machine Learning Algorithms. 
  • Bayesian Models, (Hidden) Markov Models were first studied as part of Probability Theory. 
  • The study of Logic acts as the basis for many Knowledge Based Machine Learning Paradigms:
    • Explanation Based Learning
    • Relevance Based Learning
    • Inductive Logic Programming


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