MIT 18.02: Multivariable Calculus

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MIT’s 18.02 Multivariable Calculus extends the principles of single-variable calculus to functions of multiple variables. It introduces partial derivatives, multiple integrals, and vector calculus, which are essential for modeling real-world phenomena in physics, engineering, and data science.

course

Why this course?

  1. Essential for Machine Learning & Deep Learning • Many cost functions in deep learning involve multiple variables, requiring partial derivatives for optimization. • Gradient Descent in Higher Dimensions – In neural networks, weights are updated using gradients in multi-dimensional space. • Hessian Matrices & Second-Order Optimization – Used in advanced optimization algorithms like Newton’s method.

  2. Foundation for Probabilistic Machine Learning & Statistics • Multivariate Probability Distributions – Needed for Gaussian Mixture Models, Bayesian Inference, and Principal Component Analysis (PCA). • Expectation & Variance in Multiple Dimensions – Important for probabilistic AI models.