Code Watch: Learning machine learning - SD Times

Machine learning is, regrettably, not one of the day-to-day chores assigned to most programmers. However, with data volumes exploding, and high-profile successes such as IBM's Jeopardy-beating Watson and the recommendation engines of Amazon and Netflix, the odds are increasing that ML's opportunity might knock on your door one day. From the 1960s to the 1980s, the emphasis of artificial intelligence was in "top-down" approaches in which expertise from domain experts was somehow transcribed into a fixed set of rules and their relations. Often, these would be a series of small "if-then" rules, and the "magic sauce" of expert systems was that they could draw conclusions by automatically chaining together the execution of those rules whose "if" parameters were known. The technology for inferencing worked well enough, but it turned out that very large rulebases were hard to debug and maintain, while not very large rulebases didn't produce many compelling applications (for instance, my expert system for identifying seabirds failed to make me a billionaire).