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TinkerAndThinker t1_j9k0y39 wrote

Looking for recommendations on PhD-level papers/textbooks/reading list on Machine Learning.

I want to revisit even the most "basic" of topics such as linear/logistic regression, but with better deeper understanding.

Desired outcome: able to answer questions like

  • how to test for xxx assumption
  • what is the implication if xxx assumption is violated (eg. heteroskedascity of error terms)

TIA!

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should_go_work t1_j9zclbn wrote

Pattern Recognition and Machine Learning (PRML) and Elements of Statistical Learning (ESL) are two of the standard references that will give you what you're looking for with regards to the more classical topics you allude to (linear models, kernels, boosting, etc.).

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TinkerAndThinker t1_ja1ifrn wrote

Briefly looked through and I think you're spot on.

Please feel free to throw more leads my way, thank you!

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