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
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I graduated from BITS Pilani Goa Campus in 2015 with B. E. (Hons.) Computer Science and M. Sc. (Hons.) Mathematics. After graduation, I worked with Google from 2015 to 2020 with operations research team. With the operations research team I worked as a developer of Google OR-Tools. I Left Google in 2020 to start my PhD under the supervision of Prof. Andrea Lodi and Prof. Guy Desaulniers in applied mathematics.
My research interests:
Discrete optimization
Column Generation
Machine Learning.
