Investing my time to learn Deep Learning (CNNs, RNNs) vs. Random Forest vs. PGMs vs. Reinforcement Learning well enough to be able to apply the chosen approach, it seems that PGMs are not high in the list, is that correct?
Are there Kaggle competitions, in which PGMs have been the best approach?
What are the real-world problem areas that PGM's currently excel at compared to other methods?
I'd say for people with interest in ML and DL, the main insight from PGMs is intuitions about probability theory, especially conditional probabilities and the concept of (un)conditional independence. State of the art in which PGMs would be relevant is currently mostly brute-force learning via stochastic gradient descent (with momentum) in deep directed models/function approximators, but function approximation likely does not solve all problems as adversarial examples show. There are certainly very important applications of PGMs in all kinds of domains as listed in the link above (perhaps I would have added FastSLAM [1]), and there are also variational autoencoders/Helmholz machines and (Deep) Boltzmann Machines, which are especially interesting for research perspective.
[1] http://robots.stanford.edu/papers/montemerlo.fastslam-tr.pdf
I'd probably rank these areas in order of importance as follows: deep learning, pgms, reinforcement learning. Deep learning as a framework is pretty general. PGMs, as I have seen them, don't really have any one killer domain area - maybe robotics and areas where you want to explicitly model causality? Applications for reinforcement learning seem the more niche, but maybe that's because they haven't been adequately explored to the extent that DL/CNNs/RNNs/PGMs have been.
I'm doing a thesis on tree based algorithm and it works great for medical data.
Granted I have little exposer to NN, you can't do that with NN when clinical trials data is small as hell.
It's all base on the type data and your resource and criteria.
NN is really hype and people tend to over look other algorithms but NN is not a silver bullet. I don't get how you rank those importance either, it could be bias.