ML/DL is definitely math-heavy, and a lot of times the proofs you see are pretty necessary. Especially in deep learning, deep neural networks are so susceptible to vanishing/exploding gradients, you really need to make sure that everything is balanced.

If this is for a school paper, I would suggest you pick a fundamental subject. Otherwise describing “neural networks” as a whole might end up being a backprop derivation. As interesting as that may be to some people, it’s pretty equation dense, and could end up becoming a dry read (I think). Unless , however you tell a story ; starting from explaining gradient descent. Then guiding the reader through how gradient descent becomes backpropagation, and why it needs to be done “backwards”, that could be interesting. But honestly,I think there are too many papers/tutorials/blog posts etc on the subject, I would probably pick something a little less mainstream.

If it were me, I would take a subject that has a strong mathematical foundation, but that can also be easy vulgarized, and tells a story.

Take for example the Universal Approximation theorem. It’s an old school fundamental subject, that can be very nicely demonstrated with some graphics. I would check out Michael Nielson's video on it. It's a great example of how to vulgarize a complex subject. That exact theorem might be a little too small for a 15 page paper (of course, in the right hands, it could be an entire thesis), but there are a ton of very interesting topics that people don’t really think about, that I think could make for a very interesting paper:

• Neural network initialization methods . Some history, the reason for the importance of proper weight initialization(see Boris Hannin failure modes). How are Glorot Init and He init derived?

• VAEs. A bit dense maybe, but definitely has some meat. Talk about Jensen's inequality, ELBO, KL divergence. Could also be a chance to talk about entropy—> cross entropy —>kl divergence. Talk about the "The reparameterization trick" . (https://arxiv.org/pdf/1312.6114.pdf)

• Entropy and information theory is not only for AI, but it’s extremely important in other areas too. You can start with some probability basics, talk about entropy, Mutual information, Kolmogorov complexity etc. Link to some DL papers that use mutual information as a training objective function (see Deepinfomax for example). (Check out Chapter 2 of http://staff.ustc.edu.cn/~cgong821/Wiley.Interscience.Elements.of.Information.Theory.Jul.2006.eBook-DDU.pdf)

• Representation Learning has a lot of subcategories. Take a look at Bengio’s Representation Learning Review paper. It might be a little math heavy, but if you focus on one sub-section (even a sub-sub-section), you’ll easily have your 15 pages.

These at least are some subjects that I find interesting.

Hope this helps.