ML
ML Interview Roadmap
← Study plan·Traditional ML

Day 24 of 133

SVM, kNN, Naive Bayes + DSA Linked List

Kernel trick, curse of dimensionality, generative vs discriminative.

DSA · NeetCode Linked List

  • Linked List CycleDSA · Linked List

    Interview questions to prep

    1. Walk through Floyd's tortoise & hare — why must they meet inside the cycle?
    2. How do you find the start of the cycle (LC 142) once you've detected one?
  • Find The Duplicate NumberDSA · Linked List

    Interview questions to prep

    1. Walk through your pointer hazards — what breaks if you lose track of the head or a prev pointer?
    2. Can you do this in-place (O(1) extra space)? What's the trick?
    3. How would you detect / handle a cycle, and prove your method's correctness?

ML · SVM, kNN, Naive Bayes

  • SVM: max-margin, kernel trick, soft marginTraditional MLStatQuest

    Interview questions to prep

    1. What's the geometric intuition behind the max-margin SVM?
    2. Explain the kernel trick — why it lets us work in infinite-dimensional spaces without computing them.
    3. When would you NOT use an SVM today?
  • kNN: distance metrics, curse of dimensionalityTraditional MLRead

    Interview questions to prep

    1. Why does kNN degrade in high dimensions (curse of dimensionality)?
    2. How would you choose k and the distance metric for kNN?
  • Naive Bayes: when 'naive' actually worksTraditional MLStatQuest

    Interview questions to prep

    1. Why does Naive Bayes still work despite the unrealistic independence assumption?
    2. Compare generative vs discriminative classifiers using NB and Logistic Regression.

References & further reading