Machine Learning (ML), Artificial Intelligence (AI), Deep Learning : definitions - villageprogrammer.tech

Machine Learning

• Subset of Artificial Intelligence (AI) Techniques which uses statistical methods to enable machines to improve with experience.

Machine learning relies on algorithms to analyze huge data-sets. Currently, machine learning can’t provide the sort of AI that the movies present. Even the best algorithms can’t think, feel, present any form of self-awareness, or exercise free will. What machine learning can do is perform predictive analytics far faster than any human can. As a result, machine learning can help humans work more efficiently. The current state of AI, then, is one of performing analysis, but humans must still consider the implications of that analysis — making the required moral and ethical decisions.

The “Considering the Relationship between AI and Machine Learning” section of this chapter delves more deeply into precisely how machine learning contributes to AI as a whole. The essence of the matter is that machine learning provides just the learning part of AI, and that part is nowhere near ready to create an AI of the sort you see in films.

• The name Machine Learning was coined by "Arthur Samuel"  in 1959 .

• Tom M. Mitchel provides  formal definition of Machine Learning as :

A computer program is said to learn from Experience E with respect to some class of Tasks T and Performance Measure P, if it's performance  at tasks in T, as measured by P , improves with experience E .

Example : Playing Checkers.

E :  The experience of playing many games of checkers.
T :  The task of playing checkers.
P :  The probability that the program will win the next game. 

Artificial Intelligence

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Any field that enables computers to mimic human behavior.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

• Knowledge
• Reasoning
• Problem solving
• Perception
• Learning
• Planning
• Ability to manipulate and move objects.

Approaches :

• Cybernetics and brain simulation
• Symbolic
• Sub-symbolic
• Statistical learning
• Integrating the approaches

Deep Learning

Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data.

Deep learning is a specific approach used for building and training neural networks, which are considered highly promising decision-making nodes. An algorithm is considered to be deep if the input data is passed through a series of non-linearity or nonlinear transformations before it becomes output. In contrast, most modern machine learning algorithms are considered "shallow" because the input can only go only a few levels of subroutine calling.

Deep learning removes the manual identification of features in data and, instead, relies on whatever training process it has in order to discover the useful patterns in the input examples. This makes training the neural network easier and faster, and it can yield a better result that advances the field of artificial intelligence.