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About this interview
A technical interview on Classic ML Algorithms, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Compare K-Means, hierarchical clustering, and DBSCAN including their sensitivity to hyperparameters
Explain PCA, ICA, and t-SNE and what each is best suited for
Describe Gaussian Mixture Models and their connection to the EM algorithm
Discuss how to evaluate clustering quality in the absence of ground-truth labels
Topics covered
Unsupervised Learning OverviewK-MeansClustering EvaluationHierarchical ClusteringDBSCANPCAICAt-SNEt-SNE vs UMAPGMMGMM / EMHigh-Dimensional ClusteringDimensionality Reduction MotivationPCA + Clustering
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“What is the core difference between supervised and unsupervised learning, and what kinds of problems does unsupervised learning solve that supervised cannot?
“Explain what PCA is doing geometrically. What does it mean for the principal components to be orthogonal, and why is that useful?
“What does it mean for GMM to produce soft cluster assignments rather than hard ones, and when is that distinction practically important?