Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Gate Data Science and AI
·
Module 3
Mathematical Foundations: Calculus for Optimization
1
Derivatives of Common Functions
Compute derivatives of polynomial, exponential, logarithmic, and trigonometric functions.
2
Differentiation Rules: Chain, Product, and Quotient
Apply the chain rule, product rule, and quotient rule for differentiation.
3
Partial Derivatives and Gradients
Compute partial derivatives and gradients of multivariable functions.
4
Finding Local Extrema with Derivative Tests
Find local maxima and minima of single-variable functions using derivative tests.
5
Understanding Gradient Descent
Explain the concept of gradient descent for function optimization.
6
Lagrange Multipliers for Constrained Optimization
Solve constrained optimization problems using the method of Lagrange multipliers.
7
Fundamental Theorem of Calculus: Integration
Evaluate basic definite and indefinite integrals using the Fundamental Theorem of Calculus.
8
Trapezoidal Rule for Numerical Integration in Python
Implement numerical integration in Python using the trapezoidal rule.
Previous module
Mathematical Foundations: Linear Algebra
Next module
Probability and Statistics with Python