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.

At a Glance:
Institution: IsraelX
Subject: Computer Science
Level: Introductory
Prerequisites:
None
Language: English
Video Transcript: English
Associated skills:Localization, Algorithms, Computer Science, Particle Filter, Kalman Filter

What You’ll Learn:
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.