Machine Learning is the sub-field of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed."

Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding application routines with a specific set of instructions to accomplish a particular task, the machine is "trained" using a large training dataset and algorithms that give it the ability to learn how to perform the task.[2]

We see very little different in Machine Learning and Artificial Intelligence

Machine Learning evolved from the study of pattern-recognition and computational learning theory in Artificial Intelligence.

Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs.

Machine Learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible.

Code SymbolMath SymbolDefinitionDimensions
XXInput Data, each row in an example(numExamples, inputLayerSize)
yytarget data(numExamples, outputLayerSize)
W1W[1]Layer 1 weights(inputLayerSize, hiddenLayerSize)
W2W[2]Layer 2 weights(hiddenLayerSize, outputLayerSize)
z2z[2]Layer 2 activation Function(numExamples, hiddenLayerSize)
a2a[2]Layer 2 activity(numExamples, hiddenLayerSize)
z3z[3]Layer 3 activation Function(numExamples, outputLayerSize)
JJCost function(1, outputLayerSize)


Artificial Intelligence

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« This page (revision-11) was last changed on 10-Dec-2017 09:39 by jim