Effective machine learning algorithms

Some of the most effective machine learning algorithms beyond deep learning include:

‘random forests’ that create multitudes of decision trees to optimise a prediction;
‘Bayesian networks’ that use a probabilistic approach to analyse variables and the relationships between them; and
support vector machines that are fed categorised examples and create models to assign new inputs to one of the categories.

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Why is AI important?

Example applications of AI include the following; there are many more.

Reasoning: Legal assessment; financial application processing; games; autonomous weapons systems.
Knowledge: Medical diagnosis; media recommendation; purchase prediction; fi...

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How does deep learning work?

Deep learning involves using an artificial ‘neural network’ — a collection of ‘neurons’ connected together.

An artificial neuron has one or more inputs. It performs a mathematical calculation based on these to deliver an output. The input-output function can vary. A neuron ...

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Artificial intelligence

Coined in 1956 by Dartmouth Assistant Professor John McCarthy, ‘Artificial Intelligence (AI) is a general term that refers to hardware or software that exhibits behavior that appears intelligent.

Basic ‘AI’ has existed for decades, via rules-based programs that deliver rudi...

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Machine Learning: offloading optimisation

Machine learning (ML) is a subset of AI. All machine learning is AI, but not all AI is machine learning. Interest in ‘AI’ today reflects enthusiasm for machine learning, where advances are rapid and significant.

The goal of most machine learning is to develop a pred...

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