HACKER Q&A
📣 yuseq

Resources on Stats for MLEs?


Hey everyone, I have a couple of years of experience as a machine learning engineer; recently I noticed that I have a huge gap in knowledge when it comes to statistics that I would like to fill. Some examples of what I'm looking for:

- How to sample the data to solve a given ML problem?

- What are the underlying assumptions of some ML methods with respect to my dataset, that I should verify before I use them?

- How to find out and fix problems that can occur to the model due to inappropriate usage of some methods or bad assumptions?

I'm looking for things that DO NOT focus on things like:

- Neural networks

- Convex optimization

- Calculus/Algebra

I think what would be the best is something practical and from MLE perspective. Do you have any suggestions what resources would be best for me to fill this gap?

  👤 pizza Accepted Answer ✓
4 trustworthy pdfs of very high quality that I've used as resources to fill my gaps:

- The 3 Kevin Murphy textbooks on Probabilistic Machine Learning

- Deep Learning by Goodfellow et al

I think they do contain some of the nuggets you're looking for, and have explanations too. I typically just use the index to find what I'm looking for but you could def pass them through a pdf llm tool to have natural language search/extract answers too eg PocketLLM [0]

[0] https://www.thirdai.com/pocketllm/