Before diving into vectors, derivatives, or probability distributions, it helps to know why maths matters for AI in the first place. Every AI model comes down to the same loop: represent data as numbers, measure how wrong the model is, and adjust it to be less wrong. Maths is the language for each of those steps.
The lessons below start with roadmap-style overviews of the four pillars you'll meet in this course: linear algebra (how data and models are represented), calculus (how models learn), probability (how uncertainty is handled), and statistics (how results are judged). They then close with an animated introduction to linear algebra to carry you into the next module.
You don't need to master any of this yet. Watch the lessons in order to build the big picture, then continue on to Vectors & Matrices.
A roadmap of the essential maths skills for AI and machine learning, covering linear algebra, calculus, probability, and statistics — a good first stop to see the whole map.
Video 6minAn Amrita Online session with Dr. Manju Biya on how linear algebra structures data, calculus drives learning, probability manages uncertainty, and optimization finds the best solutions.
Video 1h 12minThe kickoff to a Mathematics for Machine Learning series, introducing the core areas: linear algebra, calculus and optimization, and probability.
Video 8minAn animated introduction to why linear algebra sits at the heart of machine learning, and a preview of a long video roadmap for learning it step by step.
Video 2min