Looking for course selection for my final two semesters of school as a CS major.

So far I’ve only completed a course on Machine Learning (similar level to Stanford’s CS229) and the rest have been CS courses not related to ML. Aside from discrete maths,I haven’t taken any additional math courses and all of it has been self-taught (probability, linear algebra, calculus).

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My goal is to graduate having a deep understanding of how ML algorithm work at the mathematical level and be able to understand most of the maths in ML papers. I’m not looking to do a PhD per se, but I’d like to be more of an “academic ML engineer”. My particular interests are ML and NLP applied in healthcare.

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With all that said, which courses should I pick over the next two semesters to optimize my goals? Keep in mind that I’ll be doing applied ML research under a professor in both semesters as well (likely to do with analyzing text in the healthcare setting). In an ideal world I’d take all these courses because they all seem super interesting, but with limited time, I’d rather pick the ones which will give me a solid foundation so I can self-learn the others later on.

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Spring 2020 (Pick two):

Computational Linguistics (NLP)

Deep Learning for Data Science

Modern Convex Optimization

Bayesian Statistics

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Fall 2020 (Pick two):

Elements of Probability Theory and Random Processes

Mathematical Statistics

Introduction to Optimization Theory

Advanced Analysis

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Thanks!