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Signal Processing Vs Machine Learning as a career

  1. Mar 1, 2014 #1

    I have an undergraduate degree in EE and I have a passion for mathematics. I'm trying to decide if I should do a masters in signal processing or machine learning. What are the pros and cons of each given the way the industry is? (I live in Bangalore, the tech capital of India, where there are tons of software companies).

    I have liked what I've seen of signal processing so far, but my concern is that there may not be as many signal processing jobs as there may be machine learning jobs in my neck of the woods, since ML is primarily software based. The last thing I want to do is complete a masters in signal processing and end up doing generic coding in some nondescript software company. Or I could hedge my bets and do signal processing for embedded systems (there are lots of those companies around here), but this may not go into the details of signal processing.

    So, to summarize: Which of these areas is more mathematical? Which has better career prospects? Which has more elegant theory? These are my main criteria for deciding between the two.

  2. jcsd
  3. Mar 1, 2014 #2
    In my personal point of view, cubicle farms are terrible places to work. I like to put my hands on things. But I'm not sure you feel the same way.

    However, you seem to be seeking a more "mathematical" field. News flash: they can both be as theoretical and mathematically complex as you'd like them to be. Personally, I would tend toward the signal processing side of things, but I am very biased because I am a ham radio enthusiast. I think silly impractical applications, such as recovering a signal reflected off of the moon, are really cool.

    Neverthless, there is a broader perspective, please see post #20 in this thread: https://www.physicsforums.com/showthread.php?t=739971&page=2

    If I were in your place, I would choose the field that excites me the most. I don't know much about machine learning algorithms, but I do know that signal processing and information theory can be wonderfully complex, beautiful and fascinating.
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