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Roi Yozevitch
“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.
Read more
“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.
Read more
“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
- 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
Register for this course and see more details by visiting:
OpenCourser.com/course/vatinn/bayesian
What's inside
Learning objectives
- 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
- 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
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches common Bayesian algorithms used for the localization of self-driving cars and autonomous vehicles
Highly relevant in the field of self-driving cars and autonomous vehicles
Includes many questions and exercises, as well as programing assignments
Develops a strong foundation in the math and programming behind these algorithms
Does not give any information or assessment of software versions
Seems to assume learners are programmers already
Save this course
Save Bayesian Algorithms for Self-Driving Cars to your list so you can find it easily later:
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Reviews summary
Informative video lectures
Learners say that video lectures have excellent content and cover a wide range of topics in Bayesian Algorithms for Self-Driving Cars. However, these lectures could be improved by providing sufficient explanations for answering questions.
Informative video lectures.
"The video lecture had excellent content, covering a wide range of topics."
Insufficient explanations for questions.
"However, it fell short in providing sufficient explanations for the answers to questions."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Bayesian Algorithms for Self-Driving Cars with these
activities:
Review the mathematics of probability and statistics
Show steps
Solidifies foundation before beginning course
Browse courses on
Probability
Show steps
-
Review probability axioms and theorems
-
Practice solving probability problems
-
Review statistical concepts and methods
-
Practice solving statistical problems
Solve practice problems on Bayesian probability
Show steps
Provides practice applying Bayesian concepts
Show steps
-
Find a collection of practice problems
-
Solve the problems using Bayesian methods
-
Check your answers and identify areas for improvement
Create a visual representation of the Kalman filter algorithm
Show steps
Helps build a deeper understanding of Kalman filter
Browse courses on
Kalman Filter
Show steps
-
Choose a visual medium (e.g., flowchart, diagram, animation)
-
Map the steps of the Kalman filter algorithm to the visual representation
-
Test the visual representation with sample data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on particle filters and Monte Carlo localization
Show steps
Provides hands-on experience with particle filters
Browse courses on
Particle Filters
Show steps
-
Find tutorials on particle filters and Monte Carlo localization
-
Follow the tutorials to implement particle filters
-
Test the particle filters with sample data
Compile a list of resources on Bayesian algorithms
Show steps
Provides a valuable reference for further learning
Browse courses on
Resources
Show steps
-
Gather resources on Bayesian algorithms from various sources
-
Organize the resources into a structured format
-
Share the compilation with peers and other learners
Create a DIY particle filter
Show steps
Apply conceptual knowledge to a hands-on project to improve understanding and refine skills
Browse courses on
Particle Filter
Show steps
-
Plan and gather necessary materials
-
Code and program the particle filter
-
Run the filter and test performance
Develop a self-driving car simulation using particle filters
Show steps
Applies course concepts to a practical project
Browse courses on
Particle Filters
Show steps
-
Design the simulation environment
-
Implement particle filters for localization
-
Test the simulation with different scenarios
-
Analyze the results and make improvements
Review the mathematics of probability and statistics
Show steps
Solidifies foundation before beginning course
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability axioms and theorems
- Practice solving probability problems
- Review statistical concepts and methods
- Practice solving statistical problems
Solve practice problems on Bayesian probability
Show steps
Provides practice applying Bayesian concepts
Show steps
Show steps
Show steps
- Find a collection of practice problems
- Solve the problems using Bayesian methods
- Check your answers and identify areas for improvement
Create a visual representation of the Kalman filter algorithm
Show steps
Helps build a deeper understanding of Kalman filter
Browse courses on
Kalman Filter
Show steps
Show steps
Show steps
- Choose a visual medium (e.g., flowchart, diagram, animation)
- Map the steps of the Kalman filter algorithm to the visual representation
- Test the visual representation with sample data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on particle filters and Monte Carlo localization
Show steps
Provides hands-on experience with particle filters
Browse courses on
Particle Filters
Show steps
Show steps
Show steps
- Find tutorials on particle filters and Monte Carlo localization
- Follow the tutorials to implement particle filters
- Test the particle filters with sample data
Compile a list of resources on Bayesian algorithms
Show steps
Provides a valuable reference for further learning
Browse courses on
Resources
Show steps
Show steps
Show steps
- Gather resources on Bayesian algorithms from various sources
- Organize the resources into a structured format
- Share the compilation with peers and other learners
Create a DIY particle filter
Show steps
Apply conceptual knowledge to a hands-on project to improve understanding and refine skills
Browse courses on
Particle Filter
Show steps
Show steps
Show steps
- Plan and gather necessary materials
- Code and program the particle filter
- Run the filter and test performance
Develop a self-driving car simulation using particle filters
Show steps
Applies course concepts to a practical project
Browse courses on
Particle Filters
Show steps
Show steps
Show steps
- Design the simulation environment
- Implement particle filters for localization
- Test the simulation with different scenarios
- Analyze the results and make improvements
Career center
Learners who complete Bayesian Algorithms for Self-Driving Cars will develop knowledge and skills
that may be useful to these careers:
Perception Engineer
Perception Engineer
Perception Engineers design, develop, and maintain perception systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Perception Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Perception Engineers develop the skills needed to design and develop perception systems for autonomous vehicles.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Autonomous Vehicle Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Autonomous Vehicle Engineers develop the skills needed to develop and test autonomous vehicles.
Simulation Engineer
Simulation Engineer
Simulation Engineers design, develop, and maintain simulation systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Simulation Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Simulation Engineers develop the skills needed to design and develop simulation systems for autonomous vehicles.
Planning Engineer
Planning Engineer
Planning Engineers design, develop, and maintain planning systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Planning Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Planning Engineers develop the skills needed to design and develop planning systems for autonomous vehicles.
Verification Engineer
Verification Engineer
Verification Engineers design, develop, and maintain verification systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Verification Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Verification Engineers develop the skills needed to design and develop verification systems for autonomous vehicles.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design, develop, and maintain control systems for a variety of applications, including robotics, manufacturing, and transportation. Control Systems Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Control Systems Engineers develop the skills needed to design and develop control systems for autonomous vehicles.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Systems Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Systems Engineers develop the skills needed to design and develop systems for autonomous vehicles.
Test Engineer
Test Engineer
Test Engineers design, develop, and maintain test systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Test Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Test Engineers develop the skills needed to design and develop test systems for autonomous vehicles.
Safety Engineer
Safety Engineer
Safety Engineers design, develop, and maintain safety systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Safety Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Safety Engineers develop the skills needed to design and develop safety systems for autonomous vehicles.
Validation Engineer
Validation Engineer
Validation Engineers design, develop, and maintain validation systems for autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Validation Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Validation Engineers develop the skills needed to design and develop validation systems for autonomous vehicles.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They may work in a variety of industries, including technology, finance, and healthcare. Machine Learning Engineers typically need a master's degree in computer science, statistics, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Machine Learning Engineers develop the skills needed to develop machine learning models for autonomous vehicles.
Research Scientist
Research Scientist
Research Scientists conduct research on autonomous vehicles. They may work in a variety of industries, including automotive, technology, and transportation. Research Scientists typically need a PhD in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Research Scientists develop the skills needed to conduct research on autonomous vehicles.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data to help businesses make informed decisions. They may work in a variety of industries, including technology, finance, and healthcare. Data Scientists typically need a master's degree in data science, statistics, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Data Scientists develop the skills needed to analyze data from autonomous vehicles.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may work in a variety of industries, including technology, finance, and healthcare. Software Engineers typically need a bachelor's degree in computer science or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Software Engineers develop the skills needed to develop software for autonomous vehicles.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots for a variety of purposes. They may work in research and development, manufacturing, or field service. Robotics Engineers typically need a bachelor's degree in engineering, computer science, or a related field. “Bayesian Algorithms for Self-Driving Cars” may be useful in this role because it can help Robotics Engineers develop the skills needed to program robots that can navigate complex environments.
For more career information including salaries, visit:
OpenCourser.com/course/vatinn/bayesian
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bayesian Algorithms for Self-Driving Cars.
This comprehensive and advanced textbook covers the theory and techniques of Bayesian statistics, providing a deep understanding of the Bayesian approach.
Provides a comprehensive overview of Monte Carlo methods, including Markov chain Monte Carlo, for Bayesian inference and statistical modeling.
A comprehensive introduction to probability theory, covering fundamental concepts and advanced topics like Bayesian inference.
This textbook provides a rigorous foundation in statistical inference, including frequentist and Bayesian approaches, making it a valuable resource for understanding Bayesian algorithms.
This comprehensive textbook covers mathematical foundations for machine learning, including linear algebra, probability, and optimization, providing a strong mathematical background for Bayesian algorithms.
This widely used textbook provides a comprehensive overview of machine learning concepts and algorithms, including Bayesian methods, offering a broader perspective on self-driving car algorithms.
Save
This textbook provides a comprehensive overview of computer vision algorithms and applications, including topics relevant to self-driving cars, such as image processing and object recognition.
This practical guide provides hands-on experience with robotics, vision, and control algorithms using MATLAB, enhancing the understanding of self-driving car systems.
This classic textbook introduces probabilistic methods for robotics, including localization and mapping, providing a theoretical foundation for Bayesian algorithms used in self-driving cars.
This practical guide focuses on implementing machine learning algorithms using popular Python libraries, providing hands-on experience that can enhance the understanding of Bayesian algorithms for self-driving cars.
This introductory book provides a practical approach to Bayesian methods using probabilistic programming, making Bayesian algorithms more accessible and relatable.
This interactive book uses Python to teach Bayesian statistics, providing a hands-on approach to understanding Bayesian algorithms and their application in self-driving cars.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vatinn/bayesian
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Effort
2 to 3 hours per week for 13 weeks
Level
Introductory
Via
edX
Institution
IsraelX
Instructor
Roi Yozevitch
Language
English
Transcript
English
Good to know
Teaches common Bayesian algorithms used for the localization of self-driving cars and autonomous vehicles
Highly relevant in the field of self-driving cars and autonomous vehicles
Includes many questions and exercises, as well as programing assignments
Develops a strong foundation in the math and programming behind these algorithms
Does not give any information or assessment of software versions
Seems to assume learners are programmers already
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