Discover how to implement multivariate calculus, derive function representations of calculus, and utilize differentiation and linear algebra to optimize machine learning (ML) algorithms.

ML Algorithms: Multivariate Calculation & Algorithms

recognize the role of multivariate calculus in machine learning

describe functions in calculus

define the concepts of gradient and derivative and describe their applications on the functions of variables

list the capabilities of the product and chain rules

define partial differentiation and its application in vector calculus and differential geometry

recognize the importance of linear algebra in machine learning

describe optimization techniques when using Gradient and Jacobian matrix

define Taylor's theorem and specify the conditions for local minima

list various multivariate operations that can be used in multivariate calculus, compare the differences between a gradient and derivative, recall examples of partial differential equation, and specify the domains where linear algebra is implemented