Topics
Follow a thread.
Topics group related concepts into a suggested sequence — so you always know what comes next.
Calculus
Core Calculus
The essential sequence from limits through integration — the ideas that power all of modern science and engineering.
Differential Equations
From derivatives to equations that model change — the mathematics of everything that moves, grows, or decays.
Optimization
The mathematics of finding minima and maxima — from gradient descent's calculus to linear programming's algebra.
Integral Transforms
Rewriting a function through an integral against a kernel — turning calculus problems (and differential equations) into algebra.
Signals and Systems
Algebra
Linear Algebra
Vectors, matrices, and the geometry of high-dimensional space — the language of machine learning and modern data science.
Number Theory
The properties of integers — divisibility, primes, and modular arithmetic — that underlie cryptography and abstract algebra.
Discrete Math & Graph Theory
Counting, logic, and structure — the mathematics of finite and countable things, from proofs to networks.
Statistics
Probability & Inference
How to reason under uncertainty — from counting outcomes to drawing conclusions from data.
Regression
Predicting continuous outcomes — from the simplest straight line to regularized models that generalize.
Bayesian Statistics
Treating unknowns as probability distributions and updating beliefs with data — from Bayes' theorem through modern computational methods.
Probabilistic & Statistical Modeling
Modern statistical models for latent structure, time-varying systems, causal questions, uncertainty-aware prediction, and nonlinear data geometry.
Information Theory & Stochastic Processes
Measuring uncertainty in bits, and modelling systems that evolve randomly over time — entropy, channels, random walks, and Markov chains.
Time Series
Modelling data that unfolds over time — trends, autocorrelation, and forecasting with ARIMA.
Machine Learning
ML Foundations
The core ideas every machine learning practitioner needs — understanding what makes a model good, bad, or overfit.
Supervised Classification
The main toolkit for predicting categories — from probabilistic classifiers to geometric decision boundaries.
Deep Learning
Layered, trainable function approximators — how neural networks are built and trained via backpropagation.
Clustering
Finding hidden structure in unlabelled data — partition, hierarchy, density, and probabilistic approaches.