∑Linear Algebra
Vector spaces, matrix factorization, eigenvalues and eigenvectors, least squares
embeddings, PCA, neural network layers
ℙProbability & Statistics
Random variables and distributions, estimation, hypothesis testing, regression
uncertainty, A/B testing, model evaluation
∇Calculus & Optimization
Multivariable and vector calculus, gradients, constrained optimization
gradient descent, backpropagation, loss functions
∂Differential Equations
Ordinary and partial differential equations, analytical solution methods
time-series and physics-informed models
≈Numerical Methods
Root finding, numerical integration, iterative solvers, error analysis
stable, efficient scientific computing
⟳Dynamical Systems & Control
Process dynamics, transfer functions, feedback control, dynamic simulation
forecasting and control of industrial processes
⚗Physical Systems Modeling
Mass and energy balances, thermodynamics, kinetics, transport phenomena
domain features and constraints for industrial ML
◬Statistical Learning
Linear models, kernels, neural networks, clustering, generalization
the theory behind model choice and tuning
