Finally, analogizers search for similarities in data and write code that combines them to make new predictions.

Bayesians know that all learned knowledge is uncertain, so they emphasize 18th-century English clergyman Thomas Bayes’ theorem, which can handle probabilistic inference. Evolutionaries write programs that change in ways similar to natural selection. Connectionists try to reverse-engineer the brain. Symbolists believe that intelligence emerges from manipulating symbols, just as mathematicians solve equations by replacing expressions with other expressions. Though lucid and consistently informative, Domingos’ explanation of how a variety of scientific schools approaches the master algorithm requires close attention from readers. Defeating the world’s greatest chess or Jeopardy players was a matter of brute force, but simpler computers running learning programs already beat talent scouts in baseball, connoisseurs in wine tasting, and doctors in disease diagnosis. of Washington), is the master algorithm, and the process is well underway to allow computers to function creatively. The ultimate learning program, writes Domingos (Computer Science/Univ. As the author notes in this enthusiastic but not dumbed-down introduction to machine learning, it is impossible to “write a program to tell a computer how to drive a car or decipher handwriting, but if we give enough examples to a computer running a learning algorithm, it will figure out how to do it on its own.” Traditionally, the only way to make a computer execute a task is to write precise instructions: an algorithm.

The master algorithm book