Saturday, October 10, 2015

Introduction to Machine Learning

Understanding Machine Learning

What is Machine Learning?

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. 

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -- Tom Mitchell, Carnegie Mellon University 

Types of problems and tasks

Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end.
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

Some examples of problems which can be solved by Machine Learning

  • optical character recognition: categorize images of handwritten characters by the letters represented
  • recommendation: Amazon’s product recommendation 
  • face detection: find faces in images (or indicate if a face is present)
  • spam filtering: identify email messages as spam or non-spam
  • topic spotting: categorize news articles (say) as to whether they are about politics, sports, entertainment, etc.
  • spoken language understanding: within the context of a limited domain, determine the meaning of something uttered by a speaker to the extent that it can be classified into one of a fixed set of categories
  • medical diagnosis: diagnose a patient as a sufferer or non-sufferer of some disease
  • customer segmentation: predict, for instance, which customers will respond to a particular promotion
  • fraud detection: identify credit card transactions (for instance) which may be fraudulent in nature
  • weather prediction: predict, for instance, whether or not it will rain tomorrow
There is many research going on in field of Machine Learning in throughout the world. If Machine Learning excite you, then there is many free courses available

1 comment:

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