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Understanding the importance of decision-making
Identifying biases that affect decision-making
Analyzing the potential outcomes of a decision
The instructions tell a computer how to transform a set of facts into useful information.
The facts are data. The useful information is knowledge for people, instructions for machines or input for another algorithm. Typical examples are sorting sets of numbers or finding routes through maps.
To a computer, input is the information needed to make decisions.
For example, if you get dressed, you will need information such as what clothes are available to you, then you might consider the temperature, the season, and personal preferences.
Computation is the heart of an algorithm and involves arithmetic, decision-making, and repetition.
To apply this to getting dressed, you make decisions by doing some math on input quantities. Wearing a jacket might depend on the temperature. To a computer, part of getting dressed algorithm would be "if it is below 50 degrees and raining, then pick the rain jacket and a long-sleeved shirt."
The last step of an algorithm is output - expressing the answer.
Output to a computer is usually more data. It allows computers to string algorithms together in complex ways to produce more algorithms. Output can also present information, such as putting words on a screen.
It can sometimes be too complicated to spell out a decision-making process. Machine learning tries to "learn" based on a set of past decision-making examples.
Machine learning is used for things like recommendations, predictions, and looking up information.
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