Before you start
Learning on edX
In this instructor-paced course, plan to complete the course within the defined time period.
What you will learn
- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations
- Inference methods
- Laws of large numbers and their applications
- Random processes
Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.
The course covers all of the basic probability concepts, including:
- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)
This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
Meet your instructors
"You won’t find another intro to probability with greater depth and breadth."
"This is a great course for those serious about forming a solid foundation in probability."
"[This course] has created a love for probabilistic models, that, I guess, truly govern everything around us."
"This should be in top 10 MOOCs of all time."
Frequently asked questions
The material covered, and the resources (videos, etc.) are largely the same, but homeworks and exams contain revised and new problems.
What textbook do I need for the course?
None - there is no required textbook. The class follows closely the text Introduction to Probability, 2nd edition, by Bertsekas and Tsitsiklis, Athena Scientific, 2008. (See the publisher's website or Amazon.com for more information.) However, while this textbook is recommended as supplemental reading, the materials provided by this course are self-contained.
What is the format of the class?
The course material is organized along units, each unit containing between one and three lecture sequences. (For those who purchase the textbook, each unit corresponds to a chapter.) Each lecture sequence consists of short video clips, interwoven with short problems to test your understanding. Each unit also contains a wealth of supplementary material, including videos that go through the solutions to various problems.
How much do I need to work for this class?
This is an ambitious class in that it covers a lot of material in substantial depth. In addition, MIT considers that the best way to master the subject is by actually solving on your own a fair number of problems. MIT students who take the corresponding residential class typically report an average of 11-12 hours spent each week, including lectures, recitations, readings, homework, and exams.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.