The second way is to actually measure and sum all three angles of a large number of triangles. Out of theoretically infinite number of triangles, you pick 100 of them randomly and do the measuring and summing. The result will be consistently same, 180 degrees. But the sample size of 100 is too few to decisively say that this is a general rule. If you increase the sample size up to 1 million, perform the same process, and get the same outcome, now your finding gains far greater credibility due to far more evidence enough to conclude that the same can be applied to the entire population of triangles. Learning generally applicable rules from the patterns hiding in sample data is known as inductive learning. Since statistics is to extract sample, find patterns, and generalize patterns, it is also called statistical learning.
So in one sentence, machine learning is a computer-run statistical learning, which means a learning process for a computer to find generalizable patterns out of given sample data. People learn both deductively and inductively. Computers can only learn inductively, but they excel in it so overwhelmingly enough to show far superior learning ability and intelligence to humans in certain tasks such as chess and go. Measuring angles of 1 million triangles is a daunting task for human requiring months of time, but for computers it is an easy job worth of less than an hour. Why do computers excel in statistical learning? Because it is by nature math and statistics, and they are in the end computation. Machine learning requires iterative computation on massive data, which computers are designed to do. After all, computer is ‘compute’ + ‘er’.
Reason 1. Big Data
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