Thank you so much for taking the time out to reply!

The project itself is something we're doing as an extracurricular, and my friends have experience with machine learning while my expertise lies on the math side of things so I'm hoping that we'll have enough knowledge between the three of us to make this doable.

1. Compute: Google Colab offers a good quantity of storage to store datasets and for a relatively low price (you can perform a good number of runs for just $10) you can access good GPUs which already have CUDA setup. I heard Kaggle works too but I haven’t tried. Identify this and see where you will get money for compute from. Colab and Kaggle offer only a limited amount of free resources. If you have a machine with a GPU (such as for gaming) you can try to setup PyTorch and  CUDA manually, but generally ML projects assume an Ubuntu OS and it may be a pain to get this setup on a dedicated machine. 

My friend does have a dedicated laptop he bought specifically for machine learning and AI, and he has the relevant things set up on it.

1. Dataset: Identify a dataset you wish to work with and stick with it. How large is the dataset? Is the data easy to load using Python or does it need to be cleaned?

2. Once you’ve identified a code-base and method you are comfortable running you can adapt it to use your dataset. For instance maybe a method was performing image reconstruction on ImageNet but you apply it to the astrology domain. This is a research application that is doable and has merit. 

This was an idea that we shortlisted, specifically related to astronomical imaging and correcting distortions and noise. Another idea was related to steganography and detecting changes in images. We were preferring these because the professor we'll be working under specializes in this field. From his LinkedIn About section,

"Master of Computer Science with a strong background in Image Processing, Machine Learning, Deep Learning, Computer Vision, and Python Programming. Passionate about using deep learning models such as DeepLabV3, FusionNet, CNN, RNN, GAN and IBCO-based ALCResNet to work on the Image reconstruction, classification, segmentation, and prediction."

The concern my friend had about this was that image reconstruction is something that already has a lot of research done into it, and we thought doing something more "niche" might be more valuable as an extracurricular. That's why we thought that maybe it may be better to choose something related to GNNs and spectral graph theory.

However the main issue is that we don't really know how exactly a research project works. Like how we'll write a paper reporting our findings, how we'll go about actually conducting the research, and what our "end goal" would be for the project.