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.

MediumTechnical

26 practiced

Implement PCA from scratch in Python using SVD. Your implementation should accept a data matrix X (n x d), optional n_components, and return transformed data and explained variance ratios. Explain why SVD on centered X is numerically preferred over eigendecomposition of the covariance for some shapes of X.

HardSystem Design

24 practiced

Design an end-to-end machine learning system for real-time fraud prediction that must serve predictions at <50ms latency at 10,000 requests/second for 1M active users. Cover offline training pipeline, feature store (online vs offline), feature freshness, model serving (batch vs streaming vs approximate), A/B testing, monitoring, and considerations to avoid training-serving skew.

HardTechnical

24 practiced

Show how L2 regularization modifies the logistic regression objective and derive the Newton–Raphson (Newton) update for L2-regularized logistic regression. Explicitly write the gradient and Hessian including the regularization term and discuss how regularization affects Hessian conditioning and convergence.

MediumTechnical

21 practiced

Explain conceptually how gradient boosting fits an additive model using gradient and (optionally) second-order derivative information. Describe how XGBoost leverages first and second derivatives of the loss during tree fitting and why second-order terms improve leaf-weight estimates and convergence.

EasyTechnical

30 practiced

Define supervised, unsupervised, and semi-supervised learning with practical business examples for each. For every example, state the types of models you might use and the primary objective (prediction, grouping, representation).

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