Advanced 15 Questions • ~6 hrs study

Research & Case Studies

How to read ML papers, discuss recent research, tackle ML system design problems, and approach real-world case studies in interviews.

1

How do you approach reading an ML research paper?

Short Answer

Use the three-pass method: 1) Skim (title, abstract, figures) for relevance, 2) Read critically (intro, method, results) for understanding, 3) Deep dive (reproduce, verify claims) for mastery.

Key Framework

  • What problem does it solve? (Motivation)
  • What's the key insight? (Innovation)
  • How does it compare to baselines? (Results)
  • What are the limitations? (Critical thinking)
  • How could this be applied? (Practical value)

💡 Memory Trick: "Paper reading = Problem → Innovation → Results → Limitations → Applications (PIRLA)"

2

Design a recommendation system for an e-commerce platform.

Short Answer

Multi-stage system: Candidate generation (collaborative filtering, content-based) → Ranking (neural ranker with user/item features) → Re-ranking (business rules, diversity) → Serving (low-latency, A/B tested).

System Components

  • Data: User interactions, item metadata, real-time signals
  • Candidate Gen: ANN (Approximate Nearest Neighbors), Two-Tower model
  • Ranking: Deep learning model with cross-features
  • Cold start: Content-based for new users/items, exploration
  • Evaluation: Offline (NDCG, MAP) + Online (CTR, revenue, engagement)

💡 Memory Trick: "RecSys = Funnel: 1M items → 1000 candidates → 100 ranked → 10 shown"

3

Discuss the "Attention Is All You Need" paper.

Short Answer

Introduced the Transformer architecture (Vaswani et al., 2017). Key innovation: Self-attention replaces recurrence entirely, enabling parallel computation and better long-range dependencies. Became foundation for BERT, GPT, and all modern LLMs.

Key Contributions

  • Scaled Dot-Product Attention: Q, K, V matrices
  • Multi-Head Attention: Multiple attention patterns in parallel
  • Positional Encoding: Sinusoidal functions for sequence order
  • Impact: Enabled GPT-4, BERT, Stable Diffusion, AlphaFold 2

💡 Memory Trick: "This paper started the AI revolution. Know it inside out for any ML interview"

Questions 4-15 continue with the same detailed format.

Topics covered: ML System Design (Search Ranking, Fraud Detection, Content Moderation), Key Papers (ResNet, BERT, GPT series, Diffusion Models), Experiment Design, Ablation Studies, and presenting research findings in interviews.

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