Math for AI
The mathematical foundations every serious AI/ML engineer needs - linear algebra, calculus, probability, statistics, information theory, and beyond.
10 Modules. Complete Coverage.
From linear algebra fundamentals to advanced learning theory - every module is ML-engineering focused.
Linear Algebra
Vectors, matrices, eigenvalues, SVD, and tensors - the computational substrate of every ML algorithm.
What you'll master
- Vectors & Vector Spaces
- Matrix Operations
- Eigenvalues & Eigenvectors
- SVD & Decompositions
- PCA from Linear Algebra
- Tensors for Deep Learning
10 lessons
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Calculus & Optimization
Derivatives, gradients, backpropagation, and the optimization algorithms that train every neural network.
What you'll master
- Derivatives & Gradients
- Chain Rule & Backpropagation
- Gradient Descent Mechanics
- Convex Optimization
- Automatic Differentiation
- Adam, SGD & Optimizers
8 lessons
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Probability Theory
Probability distributions, Bayes theorem, and sampling - the language of uncertainty in ML.
What you'll master
- Probability Axioms & Events
- Random Variables
- Expectation & Variance
- Common Distributions
- Conditional Probability & Bayes
- Sampling Methods
8 lessons
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Statistics for ML
MLE, hypothesis testing, bootstrap, regression, and causal inference for data-driven decisions.
What you'll master
- Estimation Theory & MLE
- Hypothesis Testing
- Bootstrap & Resampling
- Regression Analysis
- A/B Testing & ANOVA
- Causal Inference
8 lessons
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Information Theory
Entropy, KL divergence, cross-entropy loss, and mutual information - the math behind every loss function.
What you'll master
- Entropy & Self-Information
- KL Divergence
- Cross-Entropy & Loss Functions
- Mutual Information
- Data Compression
- Information Geometry
7 lessons
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Bayesian Statistics
Priors, posteriors, MCMC, variational inference, and Gaussian processes for uncertainty-aware ML.
What you'll master
- Bayesian vs Frequentist
- Prior & Posterior
- Bayesian Updating
- MCMC
- Variational Inference
- Gaussian Processes
8 lessons
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Statistical Learning Theory
PAC learning, VC dimension, Rademacher complexity, and why deep networks generalize.
What you'll master
- PAC Learning
- VC Dimension
- Bias-Variance Tradeoff
- Rademacher Complexity
- Regularisation Theory
- Generalisation in Deep Learning
7 lessons
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Numerical Methods
Floating-point arithmetic, numerical stability, sparse matrices - why math on computers differs from math on paper.
What you'll master
- Floating-Point Arithmetic
- Numerical Linear Algebra
- Iterative Solvers
- Numerical Differentiation
- Sparse Matrix Methods
- Condition Number & Stability
7 lessons
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Graph Theory
Graph representations, spectral theory, and the mathematical foundations of graph neural networks.
What you'll master
- Graph Fundamentals
- Graph Representations
- Graph Algorithms
- Spectral Graph Theory
- Random Graphs
- Graph Theory for GNNs
6 lessons
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Time Series Mathematics
Stationarity, Fourier analysis, ARIMA, Kalman filters, and wavelets for sequential data.
What you'll master
- Stationarity & Ergodicity
- Autocorrelation & PACF
- Fourier Analysis & FFT
- ARIMA Models
- State Space & Kalman Filter
- Wavelets & Multiscale Analysis
7 lessons
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Build the mathematical foundation your models deserve.
From linear algebra and calculus to Bayesian statistics and learning theory - no gaps, no shortcuts.
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