Probability for Machine Learning

Name: Probability for Machine Learning
Rating: 3.7 (22 reviews)

Probability refresher for machine learning.

Created byKrunal Patel

Last updated 9/2022

English

English [Auto],

What you'll learn

Refresh probability fundamentals.
Use conditional probability and Bayes' rule in machine learning
Use random variables in machine learning
Use probability distribution functions in machine learning
Manipulate multiple and interdependent random variables
Understand law of large numbers
Recall the most frequently used probability distribution functions in machine learning

Course content

3 sections • 14 lectures • 52m total length

Introduction2:18
Refresh your probability foundations for machine learning by revisiting basics, conditional probability and Bayes' rule, random variables and their distribution functions, and the law of large numbers.
Basics4:26
Define omega as the sample space and events as its subsets, with probability as favorable outcomes over total outcomes, and recognize that probabilities lie between 0 and 1.
Assignment 10:04
Conditional probability and Bayes' rule4:40
Explore conditional probability and Bayes' rule through two events A and B, deriving P(A|B)=P(A∩B)/P(B) and P(B|A)=P(A∩B)/P(A), and decompose B by A or not A to illustrate independence where P(A∩B)=P(A)P(B).
Assignment 20:04

Random variables7:27
Define random variables as functions from outcomes to real numbers, distinguishing discrete and continuous cases. Apply pmf for discrete values and pdf for continuous ones.
Expectation and Variance11:52
Compute the expectation for discrete and continuous random variables using probability mass function and probability density function, and apply linearity of the expectation to variance and constants.
Assignment 30:04
Multiple random variables10:08
Explore joint probability distributions for multiple random variables, derive marginal density and conditional probabilities, recognize independence, and construct the covariance matrix for the x vector.
Assignment 40:04

Law of large numbers5:31
explains the law of large numbers by showing the sample mean Xbar converges to mu as n grows; it derives E[Xbar]=mu and Var(Xbar)=sigma^2/n, and presents P(|Xbar−mu|>=epsilon)<=sigma^2/(n epsilon^2).
Some important distribution functions5:22
Explore key probability distribution functions essential to machine learning, including uniform, Bernoulli, and binomial distributions, and apply their mean, variance, and normal distribution formulas.
Congratulations0:21
Concludes the probability for machine learning course by highlighting the covered topics and boosting learner confidence. Encourages learners to leave reviews and ratings to help improve the course.
bonus goodies0:02

Requirements

Basic knowledge of probability
Basic real analysis (integration)

Description

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.

Probability for Machine Learning

What you'll learn

Explore related topics

Course content

Basics5 lectures • 12min

Random variables5 lectures • 30min

Other good to know stuff4 lectures • 11min

Requirements

Description

Who this course is for: