ML Algorithms: Machine Learning Implementation Using Calculus & Probability
Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level
Overview/Description
Explore the roles of probability, variance, and random vectors in implementing machine learning (ML) processes and algorithms. Discover how to apply calculus and differentiation using R and Python libraries.

Expected Duration (hours)
0.5

Lesson Objectives ML Algorithms: Machine Learning Implementation Using Calculus & Probability

recognize the importance of probability in machine learning
identify the role of probability in the Chain and Bayes rules
define the concepts of variance, covariance and random vectors
list the various estimation parameters that can be applied in machine learning, such as Likelihood and Posteriori estimation
identify the role of calculus when applied in deep learning
demonstrate the implementation of differentiation and integration in R
implement calculus, derivatives, and integrals using Python
demonstrate the use of limits and series expansions in Python
declare symbols using Python, find multiple derivatives using the diff function of SymPy, and compute indefinite integrals using the SymPy library

Course Number: it_mlmdsndj_02_enus

Expertise Level
Intermediate