Evaluation

Tutorials/Quizzes - 10%

Midsem - 25%

Endsem - 40%

Lab

1. Assignments - 25%

Resources

S.N	Topic	Slides	Notes	External References
1.	Introduction	Slides	Notes	Cost function of neural network is non-convex?
2.	Basic Calculus	Slides	Notes	How does autograd work? Difference between True and Numerical Gradient
3.	Universal Approximation Theorem-Back Propagation	Slides	Notes	Theoritical proof for UAT Visual Proof for UAT
4.	Auto Differentiation - Why Backpropagation?	Slides	Notes	Auto Differentiation A Step-by-step Introduction to the Implementation of Automatic Differentiation
5.	Jacobian and Hessian Matrix	Slides	Notes	Jacobian and Hessian How are Jacobian and Hessian Matrices used in ML
6.	Convexset and Convex Function	Slides	Notes	Understanding Convexity
7.	Taylor's Approximation	Slides	Notes	Taylor's Theorem Visualization (Change (a,N) and Observe)
8.	Quadratic Approximation	-	Notes	Quadratic Approximation using Hessian
9.	Equivalent Definitions for Convex Function	-	Notes	Practice Problems for Convex Function

Assignments&Tutorials

S.N	Material	Due Date
1.	Assignment-0	08/02/2025
2.	Tutorial-1	-