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ML Algorithms: Multivariate Calculation & Algorithms

### ML Algorithms: Multivariate Calculation & Algorithms

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

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

Expected Duration (hours)
0.6

Lesson Objectives

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
• Course Number:
it_mlmdsndj_01_enus

Expertise Level
Intermediate