The sessions introduced projective geometry, covering its axioms, duality, homogeneous coordinates, projective plane (affine plane + line at infinity), transformations, cross ratio, and applications in graphics, vision, architecture, and art.

Resources

• Geometry Revisited
• Introduction to Geometry
• Euclidean and Non-Euclidean Geometry: Development and History
• Hartshorne: Algebraic Geometry — best read alongside:
  • Shafarevich’s Basic Algebraic Geometry
  • Mumford’s Complex Projective Varieties
  • Harris’s Algebraic Geometry: A First Course

Presented by Bhavi and Smitali

The session introduces number theory, covering the Well Ordering Principle, induction, divisibility, Euclidean algorithm, modular arithmetic (including CRT and Fermat’s Little Theorem), continued fractions, Pell’s equation, totient and Möbius functions, and cryptographic applications like RSA encryption.

Resources

• Elementary Number Theory by David M. Burton

Presented By Arjun Arunachalam and Swaminath Shiju

This is based on combinatorics. Permutations, combinations, counting with repetitions, inclusion-exclusion principle, pigeon hole principle and a questions based on them.

Resources

• Enumerative Combinatorics: Volume 2 by Richard P. Stanley
• ▶️ Watch: Catalan Numbers – Visual Explanation

Presented By Ayaan Nawaz

This session covers basics of graph theory, some basic terms associated with graphs, types of graphs, paths, handshake Theorem, chromatic number and some algorithms associated with graphs traversal.

Resources

• Graph Theory by Diesel

Presented By Ananth Madhav

This session traces the path from simple perceptrons to Multi-Layer Perceptrons, guided by the Universal Approximation Theorem.
It covers neuron modeling, network learning via gradient descent, and how neural nets can approximate any function.
By the end, you’ll see how math powers AI’s ability to model complex phenomena.

Resources

• The #1 YouTube Title Mistake (And How to Fix It!)
• Lecture 2 | The Universal Approximation Theorem – CMU
• Neural Networks (3Blue1Brown)
• TensorFlow Playground – MLP Demo (tanh, circle dataset)

Presented By Hemanth