August 15, 2017

What is Machine Learning?

Machine learning is a part of what is known as “artificial intelligence”.  It is essentially a set of computer algorithms that get better and better with experience, i.e. they know how to learn, and make predictions that get better and better (more and more accurate). 

    Waycare’s technology is based on a subset of machine learning called deep learning.  It learns from a wide set of traffic data to provide predictive analytics and insights – getting better and better as it processes and collects more and more data. 

    Machine learning has evolved from pattern recognition — Pattern recognition is a “branch of machine learning that focuses on the recognition of patterns and regularities in data”.

    Another definition of machine learning is this:  A computer learns from experience if it undertakes some task, and gets better at it with experience. 

    There are three types of machine learning.  I) Supervised learning – a “teacher” gives the computer data and a desired outcome, then asks the computer to find general rules that produce the right outcome from any data set.  Ii) Unsupervised learning — the computer is told ‘you’re on your own’, and has to find structure in the data it is provided – computer, what do these data mean?   Iii)  reinforced learning — this is semi-supervised — some of the ‘right’ results are given, some are not, and the computer is turned loose to learn.

     Data mining and machine learning are not the same thing.  Machine learning focuses on prediction — what can you, computer, predict, from the data we give you?   Data mining focuses on finding previously unknown properties of the data, e.g. finding that stock market prices are correlated with the 11-year sunspot cycle. Data mining focuses on past knowledge, machine learning seeks to create new knowledge.

    Here are several use-cases in which machine learning creates value for people: 

  • Optical character recognition – software that knows how to read our handwriting
  • Face detection – finding faces in images, and recognizing whose face it is
  • Spoken language understanding –  find the meaning in what a speaker says, and possibly turn it into written text
  • Medical diagnosis
  • Market segmentation – predict which customers will respond to a promotion
  • Weather prediction – will it rain tomorrow?  Where will the hurricane make landfall?



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