What is Machine Learning?

Author: Craig Kensek

Sr. Product Marketing Manager

April 5, 2017

Techopedia defines machine learning as “an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.  Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations.”

In machine learning, the goal is to solve some complex computational task by ‘letting the machine learn”, rather than trying to understand the problem well enough to be able to write a program, which could take much longer or be virtually impossible.

The birth of machine learning – 1959

Thank the game of checkers for machine learning.  IBM employee Arthur Samuel wanted to teach a computer to play checkers.  Samuels wrote the original program on IBM’s first commercial computer, the IBM 701, but he kept winning.  (Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed.”)

Samuels wrote a program to let the computer play against itself.  The program collected data on its games and created a predictive engine, to optimize its playing and Samuels started losing. Ultimately, the program was a success; so much that IBM stock went up 15 points in one day when it was announced!

As an aside, artificial intelligence has a longer history.  In Greek mythology, Hephaestus, the Greek god of technology, built intelligent robots.  They acted as if they were alive, and often appeared to be, except for the fact that they were made out of metal. Talos, one of his creations, was built to guard the island of Crete!

Over the years, machine learning has been incorporated into a number of different industries, as the table below illustrates. These are all outside of IT.


Some of the categories of machine learning algorithms are:  Supervised machine learning algorithms, Unsupervised machine learning algorithms, and Reinforcement machine learning algorithms.

Machine learning does not equal “statistics”.  Though machine learning will use such techniques as Naïve Bayes Classifier Algorithm, K Means Clustering Algorithm, Support Vector Machine Algorithm, Linear Regression, and Logistic Regression. These go a bit beyond Stats 101!

Machine learning is NOT rules-based.  Rules are based on what a human knows about the data, and when not tuned properly, rules generate excessive alerts.  These focus on known unknowns; but what about unknown unknowns? Humans cannot predict what future attacks will look like.

”Something like IoT just would not be possible without machine learning because there is just too much data coming from all sorts of devices,” – Nick Patience, 451 Research

Machine learning uses automated and iterative algorithms, such as the above, to learn about patterns in data, detecting anomalies, and identifying a structure that may be new and previously unknown. Machine learning paired with statistical analysis identifies relationships that may otherwise have gone undetected:  can find anomalies in data that would otherwise be unrecognized or undetected.  It can surpass human capability and software engineering capability to make use of large volumes and variety of data.

Some of the major benefits of machine learning

  • Accelerated time in model development and delivery of actionable insights
  • An optimal balance between predictive accuracy, performance and cost
  • Utilization of streaming data to deliver real-time analysis
  • Reduction of risk with enterprise grade machine learning
  • Acquisition of best insights in model performance and outcome

To learn more:

“Borderless Behavior Analytics – Who’s Inside? What’re They Doing?”  A new book by Gurucul CEO Saryu Nayyar. In particular, Chapter 6 – Discovering the Unknown: Big Data With Machine Learning http://amzn.to/2nx3zgT

Why did Machine Learning Arrive Late to Predictive Security Analytics?  – Gurucul CTO Nilesh Dherange http://bit.ly/2nNhfs2

Machine Learning is Reshaping Security –  Gurucul CSO  Leslie K. Lambert http://bit.ly/2nx6q9A