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I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high level brief introduction of artificial intelligence as a whole and neural network. What i am interested in is knowing the similarities or difference between the concepts: Deep Learning, Genetic Algorithm/Programming, Artificial Neural Networks and Machine Learning. How do they relate or how they help each other? What are their particular applications?

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    $\begingroup$ This is a very broad request, covering a large range of topics. Reading a suitable textbook is probably the best way to get this kind of introductory information. $\endgroup$ – David Richerby Feb 13 '17 at 11:24
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In a nutshell, (statistical) machine learning is a certain approach to artificial intelligence. Traditional artificial intelligence incorporates hard-coded rules which are written by humans. Statistical machine learning, in contrast, has the computer learning the rules by itself using statistical means.

There are several paradigms used for statistical machine learning, such as support vector machines, artificial neural networks, and genetic algorithms. Recently a certain type of artificial neural networks, deep neural networks, has come to the fore due to unprecedented performance. Machine learning using deep neural networks is known as deep learning.

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