{"id":248,"date":"2007-01-15T19:40:51","date_gmt":"2007-01-16T01:40:51","guid":{"rendered":"http:\/\/hunch.net\/?p=248"},"modified":"2007-01-15T19:40:51","modified_gmt":"2007-01-16T01:40:51","slug":"the-machine-learning-department","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=248","title":{"rendered":"The Machine Learning Department"},"content":{"rendered":"

Carnegie Mellon<\/a> School of Computer Science<\/a> has the first academic Machine Learning department<\/a>. This department already existed as the Center for Automated Learning and Discovery<\/a>, but recently changed it’s name. <\/p>\n

The reason for changing the name is obvious: very few people think of themselves as “Automated Learner and Discoverers”, but there are number of people who think of themselves as “Machine Learners”. Machine learning is both more succinct and recognizable—good properties for a name.<\/p>\n

A more interesting question is “Should there be a Machine Learning Department?”. Tom Mitchell<\/a> has a relevant whitepaper<\/a> claiming that machine learning is answering a different question than other fields or departments. The fundamental debate here is “Is machine learning different from statistics?” <\/p>\n

At a cultural level, there is no real debate: they are different. Machine learning is characterized by several very active large peer reviewed conferences, operating in a computer science mode. Statistics tends to function with a greater emphasis on journals and a lesser emphasis on conferences which often implies a much longer publishing cycle.<\/p>\n

In terms of the basic questions driving the field, the answer seems less clear. It is true that the core problems of statistics in the past have typically differed from the core problems of machine learning today. Yet, there has been some substantial overlap, and there are a number of statisticians nowadays that are actively doing machine learning. It’s reasonably plausible that in the long term statistics departments will adopt the core problems of machine learning, removing the reasons for a separate machine learning department.<\/p>\n

The parallel question for computer science comes up less often perhaps because computer science is a notoriously broad field. <\/p>\n

The practical implication of a new department is the ability to create a more specific curricula, admit more specific students, and hire faculty based upon more specific interests. Compared to a computer science program, classes on programming languages, computer architecture, or graphics might be dropped in favor of classes on learning theory, statistics, etc… Compared to a statistics program, classes on advanced parameter estimation and measure theory might be dropped in favor of algorithms and programming experience. <\/p>\n

An alternative solution like “learn everything from computer science and statistics” is personally appealing to me, and I have benefitted from and recommend a broad education. However this is not practical for everyone. In my experience, a machine learning skill set is an effective specialization with which people can do important things in the world. Given this, having a department with a machine learning centered curricula seems like a good idea. At Carnegie Mellon, this is the Machine Learning department. In the future and elsewhere it may have a different name, but the value of the machine learning skill set should grow with research, improving computers, and improving data sources.<\/p>\n","protected":false},"excerpt":{"rendered":"

Carnegie Mellon School of Computer Science has the first academic Machine Learning department. This department already existed as the Center for Automated Learning and Discovery, but recently changed it’s name. The reason for changing the name is obvious: very few people think of themselves as “Automated Learner and Discoverers”, but there are number of people … <\/p>\n

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