Useful majors for postgrad study in machine learning and AI

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Discussion Overview

The discussion revolves around the selection of undergraduate majors that would be beneficial for pursuing postgraduate studies in machine learning and artificial intelligence. Participants explore various academic backgrounds and their relevance to the field, including mathematics, computer science, and other interdisciplinary areas.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Homework-related

Main Points Raised

  • One participant suggests that a background in statistics and discrete mathematics is useful for AI and machine learning, while also considering the relevance of symbolic logic.
  • Another participant asserts that AI and machine learning are typically subfields of computer science, implying that a major in computer science is essential for those fields.
  • A different viewpoint emphasizes the importance of mathematics, particularly for advanced studies in computer science.
  • Some participants note that many individuals in machine learning come from mathematics backgrounds, especially as one delves deeper into statistical learning theory and related mathematical areas.
  • There is mention of cognitive science and neuroscience as valuable areas of study, particularly for research groups focused on more mathematical aspects of machine learning.
  • One participant inquires about the importance of linear algebra versus analysis in their mathematics studies, highlighting the significance of measure theory and abstract algebra for understanding probability theory.

Areas of Agreement / Disagreement

Participants express a range of opinions on the most beneficial majors for machine learning and AI, indicating that multiple competing views remain. There is no consensus on a single path or major that is universally accepted as the best preparation for postgraduate study in these fields.

Contextual Notes

Some participants mention specific areas of mathematics and other disciplines that may be relevant, but there are unresolved questions regarding the relative importance of different mathematical topics and interdisciplinary studies.

Who May Find This Useful

Students considering postgraduate studies in machine learning and AI, particularly those evaluating their undergraduate major options and prerequisites.

yojoe
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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?

Thanks.
 
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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.
 
I'd study math and then you could get a graduate degree in CS
 
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.
 
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.

Cheers
 

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