Machine Learning

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Machine Learning is a relatively new branch of Statistics (at least by name), and focuses on using statisical patterns and probabilities to infer (or basically, take a highly educated guess) future patterns and/or desired program outputs, based on the statistics surrounding particular inputs.


Bayesian Models

Bayesian models, also known as Bayes algorithm, is a basic method of infering outputs based on known/provided inputs. It breaks up probabilities of outcomes into grids of percentage of likelihood of occurence, providing a somewhat reliable but still quite basic method to evaluate (or learn) about what outputs are best for a given situation.

Neural Networks




Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.


As a tool for providing desktop activity awareness, Zeitgeist runs as a service which logs users's activities and events, anywhere from files opened to websites visited and conversations.


Cloud ML


[4] [5]




External Links


  1. Google Cloud Machine Learning - now open to all with new professional services and education programs:
  2. Google Cloud Platform sets a course for new horizons:
  3. Google announces tools to "democratize" machine learning:
  4. Winning the Ride-Sharing Startup Battle with Personalization -- A Java How-to:
  5. PredictionIO -- Quick Start - Recommendation Engine Template:
  6. Getting to Know Deep Java Library (DJL):

See Also

Statistics | AI | Robotics | Semantic Web | Big Data | Data Science | Analytics | Recommender Systems | Personalization | Advertising | Marketing