Perspective on Machine Learning

  1. Rapidly emerging application area
  2. Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
  3. Requires education/retraining for more widespread adoption
  4. A lot of “word-of-mouth” development methods

Huge amounts of data sets require automation, classical analysis tools often inadequate. High energy physics hit this wall in the 90’s. In 2009 single top quark production was determined via Boosted decision trees, Bayesian Neural Networks, etc.. Similarly, the search for Higgs was a statistical learning tour de force. See this link on Kaggle.com.