An algorithm is a set of instructions for processing information. A learning algorithm is a set of instructions that changes itself over time.
Examples:
You have a 8 red balls and 2 yellow balls in a bag. What is the algorithm for predicting the odds of a red ball?
Odds = 8 / (8+2) = 80% <- that’s because you divide the number of balls by the number of red balls.
However, after you drew 8 balls, those odds would be pretty inaccurate. A dumb algorithm would keep predicting an 80% chance of a red ball *even if we already drew all the yellow balls*.
The learning algorithm would be the same thing, except every time it draws a ball, it would remove that ball from consideration before the next attempt.
So attempt 1, odds = 8/(8+2). You draw a red ball. The algorithm changes to 7/(7+2).
Attempt 2, odds = 7/(7+2). You draw a yellow ball. The algorithm changes to 7/(7+1)
This is a very basic example, but you can see how powerful a learning algorithm can be.
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