ML
ML Interview Roadmap
← Study plan·Traditional ML

Day 21 of 133

Hyperparameter tuning — random > grid; Bayes / Hyperband

Search spaces, pruning bad runs early.

DSA · NeetCode Linked List

  • Reverse Linked ListDSA · Linked List

    Interview questions to prep

    1. Show both iterative (3 pointers) and recursive solutions. Compare stack space.
    2. What if you only want to reverse a sub-range [m, n]?
  • Merge Two Sorted ListsDSA · Linked List

    Interview questions to prep

    1. How would you generalize this to merging k sorted lists efficiently?
    2. Can you do it in-place without a dummy node? What's gained / lost?

ML · Hyperparameter tuning

  • Grid, random, Bayesian, Hyperband searchTraditional MLDistill

    Interview questions to prep

    1. Why does random search often beat grid search?
    2. When is Bayesian optimization worth the complexity over random search?
    3. How does Hyperband save compute by early-stopping bad configs?
  • Optuna / Ray Tune in practiceTraditional MLOptuna

    Interview questions to prep

    1. How would you set up a tuning study for a GBDT model with limited compute?
    2. What's the difference between Optuna's TPE sampler and a random sampler — when does TPE actually help?

References & further reading