Probability Theory vs Machine Learning for aspiring HF quant
I am a Master's student in Statistics at UC Berkeley and as an elective, I have the choice of taking either a graduate probability theory class (oof) or an undergraduate machine learning class (which is highly regarded in the data science world and gives lots of project experience over theory).
I'm leaning on the latter due to the project experience and the fact that I won't get my ass shredded, but the former still seems quite appealing if it can make me stand out among quant recruits. Thoughts?
It doesn’t matter given your program. Just do whichever you find more interesting.
The probability theory class is more useful for traditional quant work, but the ML class is generally more applicable to fields out of quant finance.
If you are someone who aspires to work in the HF Quant industry and are planning to get a job in data science in financial services, you need to allocate your time to both Machine Learning as well as Probability Theory. Machine Learning is an interdisciplinary field, which means it combines skills and knowledge from several other internet-related disciplines. It utilizes different concepts of probability, statistics, and algorithms. The concepts of these varied disciplines are used to derive insight which is then used to develop intelligent applications. On the other hand, Probability Theory helps you predict the likelihood of occurrence of future events.
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