• Length:
    13 Weeks
  • Effort:
    2–3 hours per week
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  • Course Type:
    Self-paced on your time

About this course

Skip About this course

“Bayesian Algorithms for Self-Driving Cars ” is a MOOC that will boost your skills and will prepare you for a career in the industry.

The course was designed to help students bridge the gap between "classic" algorithms and the concept of Bayesian localization algorithms.

We will explore topics such as the Markov assumption and which is utilized in the Kalman filter, the concept of Histogram filter and multi-modal distributions, the particle filter and how to efficiently program it, and many more.

In addition to many questions and exercises, we've included also 4 programing assignments so you will be able to actually program these algorithms for yourself.

What you'll learn

Skip What you'll learn
  • The concept of Bayesian Probability
  • Histogram Filters
  • The Markov Assumption
  • The Gaussian Distribution
  • Multivariate Gaussians and the covariance matrix
  • The Kalman FIlter
  • Particle Filters and Monte Carlo Localization.
  • The Extended Kalman Filter

Meet your instructors

Roi Yozevitch
Ariel University

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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.