Subfields of Machine learning

Subfields of Machine learning.

On this article you will get to know the Subfields of Machine learning.

  1. Computational learning theory.
    This subfield of Artificial Intelligence is used in the designing and analysing of the algorithms in machine learning.This computational learning theory uses supervised learning method to classify labelled data assets.
  2. Grammar induction.
    This is a machine learning process that involves the learning of formal grammar by observing the characteristics of descrete objects such as trees and graphs.A model that caters for observing such characteristics is then constructed.
  • Meta learning.

It is just like the grammar induction process only that this process involves learning of how to perform different tasks(meta skills).For instance a classifier that is trained on identifying the images of a car can look at large amounts of data and separate the cars and other data.

Machine learning fields.

  1. Adversarial machine learning.
    This is commonly used by cyber attackers when they want to cause a breach in a system.The attacker simply inputs an optical illusion kind of data to trick the user.
  2. Predictive analytics.
    This field focuses on using past recorded data to predict future outcomes.It helps to reduce the risk and contigency costs of projects.By foreseeing the outcones,the system ensures that there is proficient performance,customers are well attended to,the products and services also improve,it gains a competitive advantage over their competitors etc.
  • Quantum machine learning.
    It mainly uses specialised quantum systems to improve the speed of computation and increae the storage of data which is mainly done by the algorithms.Quantum computing uses electromagnetic signals to conduct logic operations.
  1. Development of robotics.

Robotics development uses two technologies.Numerical control and teleoperators.The numerical control is where the robot is programmed to perform the task it is intended to do whereas teleoperator is where the actual mechanism happens.There have been a number of robotics innovations such as multi-tasking bots,google worker robots,UR3 arm,Saul robot,Paro,Asus Zenbo etc.

Areas where machine learning is practiced.

  • Biomedical informatics-Machine learning reviews past medical records and helps predict patterns in various health conditions that assists medical practitioners to know how to treat them.
  • Data mining-It intergrates algorithms that help in studying data sets and predicting their patterns.There are various techniques employed in data mining for instance decsion trees,regression analysis,clustering analysis,correlation analysis,classification,outlier detection etc.
  • Email filtering.It uses the machine algorithm Naive Bayes classifiers .The algorithm is adapted here by assisting in classifying data that resemble each other.
  • Computer vision-This is mostly used in self driving cars where the cameras and sensors gather data that will help the car system to make decisisons for instance the car can sense traffic on a certain road and avoid it.
  • Customer relationship management(CRM)-It uses the data input to make predictions about certain customer trends,what customers purchase most and how to relate to them.The system should be able to make excellent decisions for customers as well as attend quickly to customers.
  • Natural Language Processing(NLP).This is a branch of Artificial Intelligence that focuses on studying theinteraction between machines and human beings.They include Dialogue systems,grammer checkers,Automatic summarisation etc.
  • Pattern recognition.It is used in making pattern prediction outcomes,classifying data that is not easy to identify and identifying images from a distance.They include Facial recognition,Handwritting recognition,Image recognition,Speech recognition,Optical character recognition.

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