So, in this respect, I think Colleges of Computing are a good thing, since computing seems vaguely equally significant as say, the arts. On the other hand, I’m a little wary about the disciplinary barriers that get erected when things like Colleges or Departments are created, and specialties spun off into their own institutions. Are we sure that ML won’t benefit from having its practitioners also trained (to some degree) in programming languages, systems engineering, and theoretical computer science? I’m willing to believe that important innovations in ML won’t necessarily come from within the core ML way of approaching problems, but may come from connections to other kinds of computing (I guess I’m primarily thinking about theory, but certainly systems and languages has something to say, as evidenced by the recent post on parallel ML algorithms).

I suppose a part of me is also reluctant to great further specializations because I’m not sure what field I rightly “belong to.” That’s a comment for another day, though, I think.

1. CMU is one of 5-6 places with *schools* of Computing (others include Utah, UC Irvine, and Georgia Tech). Being a school, CMU can choose to have departments in any sizeable area of CS. They already have units in Robotics, Software Engineering, Language Technologies, etc.

2. UC Irvine is another such place with a school of Information and Computer Science with three units – Information Science, Computer Science, and Statistics.

3. At Michigan, we have a department of Electrical Engineering and Computer Science under Engineering and separate units in Statistics (in the Literature, Sciences, and the Arts college) and Information Science (separate school).

I personally like Irvine’s model best as it brings all related units into one place.

Interesting…it would be fun to compare publishing cycles for different fields! In physics, and in particular in high energy theory and quantum computing, publication is two-fold, first on the arxiv, and then in a journal. I think the later increases the speed of the science, perhaps even beyond the CS model whose publication deadline is set by “elite” conference dates. I wonder how one would get this data…

Now, ML-only study at the undergrad level, that’s something I would have serious doubts about. At the end of the day, we still need to turn our knowledge into working software systems and I believe the engineering content of a standard CS course to be the very minimum of what is required for that.

With regards to the narrow focus of the department, the most attractive part of the PhD program to me is the lack of all the messy computer science things (i.e. Systems programming) needed for a CS degree in favor of a focus on more interesting things (like upper level courses in Statistical Learning Theory).

I dont think collaboration will suffer if ML becomes its own discipline, after all we are in the business of finding interesting things in other people’s data, and there seem to be no shortage of interesting problems (my own interests have driven me into Cognitive Neuroscience).

“No”, an algorithms department doesn’t make sense, because it is too core to the purpose of computer science. It’s difficult for me to imagine CS department without algorithms, while a CS department without machine learning is plausible.

“Yes”, a systems departments may make sense. They are often called ECE.

I don’t know about graphics and vision.

Looking at statistics relative to machine learning, statisticians don’t have the algorithms and programming experience. Can a person be broadly educated without having that experience? Maybe statistics is a narrow field. Looking at computer science, which doesn’t include any statistics, can a person be considered broadly educuated and yet still not understand how to carefully describe or think about the unknown? This isn’t idle ‘cup-is-half-fulling’. I regard ‘rogramming as the missing member of reading, ‘riting, and ‘rithmetic, and I’ve found a statistical understanding of the world genuinely valuable.

Another reason why the department might be regarded as narrow is that an ML education is not as obviously useful as a CS or statistics education. This is partly a question for the future: will an ML education become increasingly useful? I believe the answer is yes.

Don’t you think machine learning should get involved more with other fields of AI, such as planning, logic and cognitive science?

You could also argue: shouldn’t ML be more involved with computational neuroscience? Or maybe robotics? Or maybe the medicine school? Operations research? All of these connections make a lot of sense, and it would be a shame if every university forced one sort of connection and neglected the rest.

Each researcher has a finite bandwith in the number of colleagues they can work with, chat with in the halls, go to lunch with, and so on. If you spend some of that bandwidth on external collaborations, then you lose some bandwidth with other kinds of ML researchers. And so on.

I personally, would not want to be in a department with such a narrow focus (in some sense), but I can certainly understand why someone would.