Choosing Electives for Columbia MFE

Hi everyone,

I will be attending Columbia's MFE program this coming fall. Our course registration begins July 31 and I am struggling to choose between the elective concentrations offered.

The two streams I'm deciding between are:

  • Fixed Income and Term Structure Modeling
  • Beyond Black-Scholes: The Implied Volatility Smile
  • Computational Methods in Derivatives Pricing
  • Structured and Hybrid Products
  • Etc...
Machine Learning for Financial Engineering
  • Machine Learning for Financial Engineering & Operations Research
  • Deep Learning
  • Big Data in Finance
  • Data Mining
  • Etc...
I am more interested in the derivatives stream, but I feel the machine learning stream will be more useful. That being said, machine learning can be learned online effectively, whereas, I might never get a chance to learn from the experiences of industry professionals again!

I'd love to hear what you guys think. Also, feel free to leave any comments on what key topics you feel I should be comfortable before finishing my MFE.

i recommend to pay attention to the recent semesters' schedules and figure out what classes were actually offered, i.e. this page: MSFE Curriculum | Industrial Engineering and Operations Research

some classes haven't been offered for some time. you may want to know who teaches the class as well as the lecture quality of actual professors is usually far superior than the industry adjuncts.

i also recommend to take real machine learning classes from the cs department, namely coms 4771 and coms 4772 if possible. but don't forget to take programming classes like ieor 4727 and 4732