Probability for Machine Learning

Probability is usually a prerequisite of machine learning. However, one doesn’t need to know all the concepts in probability.

In this course, I have compiled together all the important probability concepts that are most frequently used in machine learning. This is the content I taught at Polytechnique Montreal as a refresher on probability for machine learning. Understanding these concepts will help you navigate through an introductory course in machine learning.

This course is for you if

– You have learned probability a long time ago

– You want to refresh the essential topics in probability to get started with your journey in machine learning.

This course is not for you if

– You want to learn probability from scratch.

– You want to master all the concepts in probability.

Please note that I do not cover all the topics in probability. I only cover the topics that are most frequently used in the machine learning textbook. If you want to learn probability from scratch or master all the concepts, this course is not for you.

In this course, we cover the following topics

Probability basics

Conditional probability and Bayes’ rule

Random variables

Expectation and Variance

Multiple random variables

Law of large numbers

Some important distribution functions

Who this course is for:

• Students who want to start their journey in machine learning and want to refresh the probability topics needed for that.