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MLOps and ML Engineering
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Module 1
Mathematical Reasoning for ML Interviews
1
Applying Bayes’ Theorem to Conditional Probability and Base Rates
Solve conditional-probability and base-rate questions using Bayes’ theorem.
2
Expectation, Variance, Covariance, and Correlation in Model Analysis
Compute and interpret expectation, variance, covariance, and correlation for model-related scenarios.
3
Maximum Likelihood and MAP Parameter Estimation
Estimate model parameters using maximum likelihood and distinguish maximum likelihood from MAP estimation.
4
Constructing and Interpreting Confidence Intervals
Construct and interpret a confidence interval for a model or experiment metric.
5
Comparing Model Variants with Statistical Hypothesis Testing
Select and interpret a statistical hypothesis test for comparing two model variants.
6
Verifying Machine Learning Computations with Vector and Matrix Dimensions
Use vector and matrix dimensions to verify the validity of common ML computations.
7
Dimensionality Reduction with Eigenvalues, Eigenvectors, and SVD
Use eigenvalues, eigenvectors, and singular value decomposition to explain dimensionality reduction.
8
Computing Gradients and Updating Parameters with Gradient Descent
Compute gradients with the chain rule and explain how gradient-based optimization updates model parameters.
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