Calculus for Machine Learning

This course lays the foundational calculus required for deep learning and continuous mathematics. It was delivered for working professionals via BITS Pilani Work Integrated Learning Programmes.

Slides are versioned — each iteration stays publicly accessible as the course improves over time.

Click a lecture to view or download the slides (PDF).

• Lecture 1: Functions & Graphs, Trig Functions

• Lecture 2: Rates of Change, Limits & Laws, Sandwich Theorem

• Lecture 3: Continuity, Limits at Infinity

• Lecture 4: Derivatives, Differentiation Rules, Chain Rule

• Lecture 5: Extreme Values, MVT, Antiderivatives

• Lecture 6: Integration, Definite Integral, FTC, Substitution

• Lecture 7: Integration by Parts, Trig Integrals, Partial Fractions

• Lecture 8: Fourier Series, Orthogonal Functions

• Lecture 9: Recap — Lectures 1–8

• Lecture 10: ODEs — Definition, IVPs, Separable Equations

• Lecture 11: Integrating Factor, 2nd-Order Linear ODEs

• Lecture 12: Laplace Transforms, 4-Step ODE Algorithm

• Lecture 13: Advanced Laplace — Unit Step, Convolution

• Lecture 14: Numerical Methods — Euler, RK4, Adams-Bashforth

• Lecture 15: PDEs — Heat, Wave, Laplace Equations

• Lecture 16: Recap — Lectures 10–15