Calculus for Machine Learning

#Learning Objectives

• Compute numerical and analytical derivatives for common ML functions (x^2, sigmoid, cross-entropy)
• Implement gradient descent from scratch to minimize a loss function in 1D and 2D
• Derive the gradient of a linear regression model and train it via manual weight updates
• Explain the Hessian matrix, Taylor series approximations, and their connection to optimization methods