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Explain core ML and DL fundamentals

DRWMachine Learning Engineer · Online Assessment
MediumMachine Learning EngineerOnline AssessmentMachine Learning

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Explain core ML and DL fundamentals evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Question

Answer the following machine-learning / deep-learning concept questions. Where useful, include the formula, the intuition, and a small worked example.

  1. PCA: What do the eigenvectors of the covariance matrix represent, and how do they relate to principal components and explained variance?
  2. Decision trees: Define Gini impurity, show how to compute it for a node, and explain how it is used to choose splits.
  3. Reinforcement learning: Write the Bellman equation (both the expectation form for a fixed policy and the optimality form for V* / Q*) and explain its role in policy evaluation and improvement.
  4. Regularization – dropout: What is dropout, how does it behave at training vs. inference time, and why does it act as a regularizer?
  5. Training stability: What is gradient clipping, when is it useful, and how do residual (skip) connections in ResNets help mitigate the vanishing-gradient problem?
  6. Optimization landscape: Why are deep-learning objectives typically non-convex, and what are the practical implications for optimization (e.g., local minima vs. saddle points)?
  7. Transformers: Describe scaled dot-product attention and multi-head attention, and explain why the dot products are scaled by 1/√d_k.