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Useful majors for postgrad study in machine learning and AI

  1. Nov 10, 2008 #1
    Hello, I'm wondering what sort of areas would be useful in postgrad study in machine learning and AI. At the moment I'm in Arts majoring in mathematics and logic/philosophy of science. I'm planning to keep these two majors, as statistics and discrete mathematics will be useful in AI/machine learning, and the decomposition of natural language into symbolic logic would also be useful. What else would be a practical major in my toolbox?

    I'm planning to pick up either a science degree (with a major in computer science and physics) or an engineering major like electrical engineering, as it is so broad of an area.

    Can anyone who has done computer science inform me if that would be the correct path to take? Should I be double majoring in Computer science? I'd kinda like to pick up the physics as well because it interests me. Are there any other majors I should be looking at, but missed?

  2. jcsd
  3. Nov 10, 2008 #2
    AI and machine learning are usually considered subfields of computer science. If that's what you want to study, that's where you should be.
  4. Nov 11, 2008 #3
    I'd study math and then you could get a graduate degree in CS
  5. Nov 11, 2008 #4
    A lot of people in machine learning have mathematics backgrounds. This is especially true as you move farther from the "applied" branches of the subject and into statistical learning theory (Vapnik-Chervonenkis theory) and related more mathematical areas.

    You'll also see a lot of people with background / interests in cognitive science / neuroscience. A lot of the best research groups, especially the more mathematical ones have a significant interest in this area. I myself am a graduate student in one of these groups. My undergraduate background was in Neuroscience, Mathematics and Philosophy.
  6. Apr 28, 2011 #5
    Hi everyone. I hope that it is ok that I drop in on this thread. I'm also very interested in machine learning, but am a maths major.

    I was wondering, should I be taking more linear algebra papers, or more analysis? I understand that measure theory is important in understanding the workings of probability theory so a lot of analysis right? But after looking around, I've seen people mention that abstract algebra would be useful to know too.

    My goal at the moment is to get into grad school so I'm trying to sort out the prerequisites so that I'm ready for it.

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