These Jupyter notebooks are my notes as I work through this stuff. I make no guarantee that these are correct. If you find an error or something that's unclear, please let me know.
- Linear Regression
- Logistic Regression
- Naive Bayes
Desision Trees- K-means
Gaussian Mixture ModelsPrinciple Component AnalysisNeural Networks
These notes attempt to follow the Ashwin-Anitha framework:
For each ML model:
Top-level
- What is the high-level version, explain in layman's terms
- What scenario should you use it in (classification vs regression, noisy data vs clean data)?
- How does this deal with outliers? Skewed data?
- What assumptions does the model make about the data? (Linear, etc)?
- What types of features does the model use (continuous vs categorical)?
- When does the model break/fail (adv & dis-advantages)?
- use cases/alternatives when it breaks
A bit more detail:
- How do you normalize the data for the model, if you need to?
- How to initialize parameters at beginning (e.g. where to put centroid in KMeans)
- What's the loss function used (if many, what are tradeoffs of each)?
- What optimizers can you use here?
- What's the complexity — runtime, parameters?
- How does it scale with # of features or input data?
In-depth
- probabilistic interpretation
- Derive the math (only for a couple models)
- Code up implementation (only for a couple models)
More on training the model (not model-specific, this should be common for most of the models):
- How to deal with imbalanced data?
- How well does it generalize to unseen data (over-fitting vs under-fitting)?
- What if you have MANY more features than sample points? Vice versa? (A variation of the above over/under fitting)
- How do you regularize the model? Tradeoffs?
- How can you validate the model?
- Does the model emphasize Type 1 or Type 2 errors?
I borrow heavily from the resources below; this includes code snippets, figures, derivations, and explanations.