Explain core ML and DL fundamentals
Overview
This question evaluates core ML and DL fundamentals — covering dimensionality reduction (PCA), decision tree impurity measures, reinforcement learning Bellman equations, regularization such as dropout, training-stability techniques, optimization landscape concepts, and transformer attention — measuring theoretical knowledge of algorithms and training dynamics. Commonly asked in the Machine Learning domain to assess foundational theory and the ability to reason about model behavior, trade-offs, and practical implications, it tests both conceptual understanding and practical application.
DRW
Jul 31, 2025, 12:00 AM
Machine Learning Engineer
Take-home Project
Machine Learning
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ML/DL Concept Questions (Take‑home)
Provide concise, correct answers to each prompt.
- PCA
- What do the eigenvectors of the covariance matrix represent?
- How do they relate to principal components and explained variance?
- Decision Trees
- Define Gini impurity.
- Show how to compute it for a node.
- Explain how it is used to choose splits.
- Reinforcement Learning
- Write the Bellman optimality equation (for V* or Q*).
- Explain its role in policy evaluation and improvement.
- Regularization (Dropout)
- What is dropout?
- How does it behave at training vs. inference time?
- Why does it act as a regularizer?
- Training Stability
- What is gradient clipping and when is it useful?
- How do residual connections in ResNets help mitigate vanishing gradients?
- Optimization Landscape
- Why are deep learning objectives typically non‑convex?
- What are the practical implications for optimization (e.g., local minima vs. saddle points)?
- Transformers
- Describe scaled dot‑product attention.
- Explain why the dot products are scaled by 1/sqrt(d_k).
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