Explain key ML concepts and techniques
Overview
This multi-part question evaluates proficiency in core machine learning competencies including algorithmic understanding (XGBoost parallelism, bandit algorithms, collaborative filtering), model training and optimization (distributed training, layer normalization, logistic regression regularization and calibration), multimodal architecture design and modality alignment, and practical considerations for scalability and evaluation. It is commonly asked in Machine Learning interviews to probe both conceptual understanding and practical application — assessing reasoning about trade-offs, communication and aggregation patterns, regularization and metrics — and therefore targets a mixed level of abstraction combining conceptual depth with implementation-oriented system-level thinking.
Onsite Machine Learning Interview: Multi-topic Questions
Answer all sections. Be precise and compare alternatives where asked. Favor concrete mechanisms over buzzwords.
1) XGBoost Parallelism and Distributed Training
Explain how XGBoost achieves parallel computation during training.
- Compare feature-parallel vs data-parallel (histogram-based) approaches.
- Discuss distributed training across machines (communication pattern, what is aggregated, how splits are found).
- Outline trade-offs in memory, speed, and accuracy (e.g., effect of binning, sparsity, and histogram subtraction).
2) Layer Normalization in Transformers
Explain layer normalization in Transformers.
- Where it is applied: pre-norm vs post-norm architectures.
- Mathematical formulation of LayerNorm.
- Why it stabilizes training and its effect on gradient flow and depth.
3) Designing a Multimodal Text–Image Model
Design a multimodal neural network that fuses text and images.
- Describe early, late, and cross-attention fusion strategies (architectural sketches, when to use each).
- How to align modalities (shared embedding spaces, contrastive pretraining, token alignment) and handle missing modalities at inference.
- Choose appropriate loss functions per task and evaluation metrics.
4) Collaborative Filtering
Explain collaborative filtering approaches.
- User-based vs item-based CF (similarities, neighborhoods, scalability).
- Matrix factorization for implicit feedback, including regularization.
- Cold-start mitigation strategies.
- Scaling to sparse, large datasets.
5) Multi-Armed Bandits
Discuss multi-armed bandits.
- Define regret.
- Compare epsilon-greedy, UCB, and Thompson sampling.
- Address non-stationary and contextual settings.
- Describe offline policy evaluation and safe deployment practices.
6) Logistic Regression
For logistic regression:
- Derive the log-likelihood and gradients.
- Compare L1 vs L2 regularization.
- Interpret coefficients as odds ratios.
- Handle class imbalance and calibration.
- Choose decision thresholds under class imbalance/costs.
Solution
Login required