"Bayesian Algorithms for Self-Driving Cars" is an advanced Computer science course, designed to equip the student with the most important localization algorithms now deployed in modern autonomous vehicles.
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“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 programming assignments so you will be able to actually program these algorithms for yourself.
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
