In our time and age, it is really hard to find a problem where machine learning is not already applied – machine learning is practically everywhere, in business applications and science. Below is a short list of the maybe most common and intuitive examples:

Computational Biology & Drug Discovery/Design

  • screening large molecule databases and identify which (drug-like) molecules are likely binding to a particular receptor protein
  • predict the potency of a receptor agonist or antagonist

(In the figure above, I rendered a crystal structure HIV protease and some potential inhibitors, PDB Code: 4TVH)

Some interesting papers if you want to read more:

  • Tarca, Adi L., et al. “Machine learning and its applications to biology.” PLoS Comput Biol 3.6 (2007): e116. (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0030116)

  • Lavecchia, Antonio. “Machine-learning approaches in drug discovery: methods and applications.” Drug discovery today 20.3 (2015): 318-331. (http://www.sciencedirect.com/science/article/pii/S1359644614004176)

Web Search and Recommendation Engines:

  • find recognize input, find relevant searches, predict which results are most relevant to us, return a ranked output
  • recommend similar products (e.g., Netflix, Amazon, etc.)

Finance

  • predict if an applicant is credit-worthy

  • detect credit card fraud
  • find promising trends on the stock market

Text and Speech Recognition

  • handwritten digit and letter recognition at the post office
  • voice assistants (Siri)
  • language translation services

(Source: https://en.wikipedia.org/wiki/Handwriting_recognition)

Space, Astronomy, and Robotics

  • autonomous Mars robots
  • identification of relevant information (objects) in large amounts of Astronomy data

(Source: https://en.wikipedia.org/wiki/Star)

Social Networks and Advertisement

  • data mining of personal information
  • selecting relevant ads to show




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