MITx course builds a systematic approach to understanding the uncertain

6.041x shows learners how to use probability to make scientifically sound predictions under uncertainty.


Many aspects of our personal and professional lives are fraught with uncertainty. Should we invest in the stock market now, or wait? How reliable are the GPS readings on a smartphone? What is the likelihood that a medical treatment will be effective? Through the new MITx course 6.041x (Introduction to Probability – The Science of Uncertainty), MIT professors John Tsitsiklis and Patrick Jaillet share the probabilistic models used to analyze these and many other uncertain situations.

While the course was developed in the Department of Electrical Engineering and Computer Science (EECS) over many decades, Tsitsiklis says the course is relevant to a much wider audience: "The class is targeted not just to EE (Electrical Engineering) students. For example, biologists need probability tools more and more."

Tsitsiklis, the Clarence J Lebel Professor of Electrical Engineering, also describes his course’s approach as unique: “We’re more ambitious than the typical undergraduate probability class. We’re different from a class that gives an overview of problems and ideas ... We aim to provide the crispest way of explaining the concepts.”

He explains that the ability to think probabilistically is a fundamental component of scientific literacy. Students in 6.041x will learn the models, skills, and tools that are the keys to analyzing data and making scientifically sound predictions under uncertainty.

Tsitsiklis is intrigued by teaching through a new medium, which may appeal to students with various learning styles. “Some people prefer to learn by reading a textbook ... Some want the encouragement of chatting with an instructor. We hope this medium (MITx) will be perfect for some people.”

The online class will offer the same content as the residential class. Tsitsiklis, who is also associate director of the Laboratory for Information and Decision Systems, has done most of the course development, while Jaillet, the Dugald C. Jackson Professor of EECS, will be responsible for managing the course once it begins. In addition, a teaching assistant, along with two undergraduates, will monitor the class forum, prepared to answer students’ questions as necessary.

6.041x will be divided into 26 lectures, of which 23 will be given by Tsitsiklis, and three will be given by Jaillet. Each lecture is divided into eight to 10 short video clips, interspersed with concept questions and simple exercises. “It will be like doing mini-homework during class,” Tsitsiklis says. “Students will have to solve the problems on the spot,” which will provide them immediate feedback. In addition, students will have access to problem-solving videos, mostly recorded by MIT graduate students, that correspond to the recitations and tutorials in the residential course.

Tsitsiklis emphasized that 6.041x will be as challenging as the residential course. “We have decided to keep it at exactly the same level,” he says, adding that the lectures will cover the same material, and students will be required to have a year of college-level calculus. “We have made the decision that this will be an ambitious, complete class, instead of a watered-down version.”

The textbook for the course, "Introduction to Probability" (2008, Athena Scientific), was co-written by Tsitsiklis and Professor Dimitri Bertsekas, the McAfee Professor of Electrical Engineering. Students will be given free online access through the edX platform, and offered a discount for purchase of a hard copy of the text.

Thus far, 18,000 people have registered to take 6.041x. Tsitsiklis expects that at least 20,000 will eventually take the course. Whether or not students complete all of the homework and tests, he still welcomes their participation. “Let everyone follow as much as they wish. It’s fine if people just want to listen and learn something," he says.


Topics: MITx, Electrical Engineering & Computer Science (eecs), Probability, EdX

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