ML
ML Interview Roadmap
← Study plan·Traditional ML

Day 17 of 133

Regularization (L1, L2, ElasticNet) + DSA Stack finish

Sparsity geometry, Bayesian priors, why ElasticNet exists.

DSA · NeetCode Stack

  • Car FleetDSA · Stack

    Interview questions to prep

    1. Why a stack here — what LIFO property does the problem exploit?
    2. If this uses a monotonic stack, state the monotonic invariant and how it's restored on each push.
    3. Walk through complexity: each element is pushed and popped at most once, so the total work is O(n).
  • Largest Rectangle IN HistogramDSA · Stack

    Interview questions to prep

    1. Walk through the monotonic stack — what does popping a bar tell you about its rectangle?
    2. How does this generalize to 'maximal rectangle in a binary matrix'?

ML · Regularization (L1, L2, ElasticNet)

  • L1 vs L2 vs ElasticNet — geometry and selectionTraditional MLStatQuest

    Interview questions to prep

    1. Why does L1 produce sparse solutions while L2 doesn't? Show the geometric picture.
    2. When do you use ElasticNet over L1 or L2 alone?
  • Early stopping & dropout as regularizersTraditional MLRead

    Interview questions to prep

    1. Why is early stopping equivalent to L2 regularization in some cases?
    2. How would you choose the early-stopping patience and what happens when it's too small or too large?
  • Regularization as a Bayesian priorTraditional MLRead

    Interview questions to prep

    1. Show that L2 regularization corresponds to a Gaussian prior on weights and L1 to a Laplace prior.
    2. When does the Bayesian framing actually change a modeling decision in practice?

References & further reading