Back to AI/ML & Deep Learning
About this interview
A technical interview on Math & Probability Foundations, pitched at the easy 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
Explain matrix multiplication, transpose, and the geometric interpretation of dot products
Describe eigendecomposition and SVD and articulate why they arise in PCA and recommendation systems
Explain the role of matrix factorization in dimensionality reduction and collaborative filtering
Define norms, gradients, and the Jacobian and explain their role in optimization
Topics covered
Matrix fundamentalsMatrix-vector multiplication geometryDot product geometryMatrix multiplicationMatrix transposeMatrix inverseMatrix rankVector normsMatrix normsEigenvalues and eigenvectorsEigendecomposition applicationsPositive semi-definite matricesCovariance matrix and eigendecompositionPCA via eigendecomposition
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In your own words, what is a matrix, and why is the idea of a matrix being a linear transformation of space useful in machine learning?
“Why are eigenvectors useful in machine learning? Can you give at least two concrete scenarios where they show up?
“Explain how matrix factorization is used in collaborative filtering for recommendation systems. What are the two matrices you are factorizing, and what do they represent?