Machine Learning Algorithms and Theory Questions

Core supervised and unsupervised machine learning algorithms and the theoretical principles that guide their selection and use. Covers linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k means clustering, hierarchical clustering, principal component analysis, and anomaly detection. Topics include model selection, bias variance trade off, regularization, overfitting and underfitting, ensemble methods and why they reduce variance, computational complexity and scaling considerations, interpretability versus predictive power, common hyperparameters and tuning strategies, and practical guidance on when each algorithm is appropriate given data size, feature types, noise, and explainability requirements.

EasyTechnical

22 practiced

Describe Lloyd's k-means algorithm in detail and discuss its sensitivity to initialization. Explain the k-means++ initialization and why it helps. Also mention computational complexity per iteration and practical tips to speed up k-means on large datasets.

HardTechnical

27 practiced

Discuss theoretical properties of random forests: under what conditions are random forests consistent? Explain how feature subsampling and tree randomness affect variance and bias, and analyze potential failure modes in high-dimensional sparse-signal regimes.

HardSystem Design

28 practiced

System design: propose a distributed architecture for training large-scale gradient-boosted tree ensembles across a compute cluster. Detail data partitioning, split-finding (exact vs histogram-based), synchronization, fault tolerance and checkpointing, and how you would support efficient hyperparameter search over the cluster.

HardTechnical

45 practiced

Research problem: boosting algorithms can overfit noisy labels. Propose algorithmic modifications to gradient boosting to improve robustness to label noise, justify your choices theoretically or intuitively, and outline an experimental protocol to validate robustness on synthetic and real noisy-label datasets.

EasyTechnical

24 practiced

As a researcher evaluating classifiers on an imbalanced binary task (rare positive class), explain the difference between ROC AUC and Precision-Recall curves. Which is more informative in the imbalanced setting and why? Describe how you would choose operating points and thresholds in practice.

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