Competencies: AI & ML > Mathematical Foundations

Linear Algebra for ML

Vectors, matrices, dot products, eigendecomposition, SVD, matrix operations in NumPy.

Critical

ML Engineer, Data Scientist

Calculus for ML

Derivatives, gradients, chain rule, partial derivatives, backpropagation mathematics.

Critical

ML Engineer, Data Scientist

Probability Theory

Probability distributions, Bayes theorem, conditional probability, expectation, variance.

Critical

ML Engineer, Data Scientist

Statistics for ML

Hypothesis testing, confidence intervals, p-values, statistical significance, A/B testing.

High

Data Scientist, ML Engineer

Optimization

Gradient descent, SGD, Adam, learning rates, convergence, local minima, convexity.

Critical

ML Engineer, Research Scientist

Information Theory

Entropy, cross-entropy, KL divergence, mutual information, loss function foundations.

High

ML Engineer, Research Scientist

Numerical Computing

NumPy, floating point precision, numerical stability, vectorization, broadcasting.

High

ML Engineer, Data Scientist

Loss Functions

MSE, cross-entropy, hinge loss, focal loss, contrastive loss, custom loss design.

High

ML Engineer, Data Scientist

Evaluation Metrics

Accuracy, precision, recall, F1, AUC-ROC, confusion matrices, business metrics alignment.

Critical

Data Scientist, ML Engineer

Regularization

L1/L2 regularization, dropout, early stopping, data augmentation, preventing overfitting.

High

ML Engineer, Data Scientist

No personal status recorded

 — 

 — 

 — 

 —