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The original was posted on /r/machinelearning by /u/Annual-Minute-9391 on 2025-01-19 00:37:06+00:00.

Some background: I work as a data scientist/ML engineer for a small startup. I also adjunct for the department from which I got my PhD(in statistics).

For the last few years, I’ve been teaching a series of statistical programming courses for masters students, and early PhD‘s. This semester, my class unfortunately got canceled due to low enrollment, which I was told is due to poor recruitment last fall and poor advertising. We are thinking to offer that course every other year. I would like to propose a third course in the series with more advanced topics.

First course: programming fundamentals for both R and Python. Some basic analytical stuff for each.

Second course: Python based analysis course (many R courses exist already) which touches on statistical routines from basics to mixed modeling and Bayesian analysis. Also we go through the classic models with PyTorch as well as a few transformer based applications. Also work in some explainable AI techniques

Third course: optimization, variational inference, other Bayesian deep learning approaches, MLops concepts, ???

The thing is I need to work in a fair amount of stochastic approaches because it’s a statistics department after all.

Hope that’s clear. I would like to provide relevant information especially to PhD students who would like to live at the cutting edge with an emphasis on experimentation and implementation. I know there is a lot out there but at work I need to focus on my specific tasks.

Thanks so much for any advice!