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David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
Find additional content here on Unscented Kalman Filters, for sensor fusion and tracking.
Register for this course and see more details by visiting:
OpenCourser.com/course/jm30m8/additional
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What's inside
Syllabus
While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Unscented Kalman Filters, a technique used for accurate tracking of non-linear motion in sensor fusion
Taught by David Silver and a team of experts from DeepMind, known for their pioneering work in reinforcement learning
Provides a strong foundation for understanding Unscented Kalman Filters and their applications in tracking
Relevant for students and practitioners in robotics, autonomous systems, and state estimation
Requires some background in linear algebra, probability theory, and Kalman filtering
Save this course
Save Additional Content: Kalman Filters to your list so you can find it easily later:
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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 Additional Content: Kalman Filters with these
activities:
Connect with experts in non-linear motion
Show steps
Enhance your learning by seeking guidance from experts in the field of non-linear motion.
Show steps
-
Identify potential mentors through professional networks or research institutions.
-
Reach out to mentors and express your interest in connecting and learning from their expertise.
Review matrix algebra
Show steps
Review the fundamentals of matrix algebra to strengthen your understanding of linear motion.
Browse courses on
Matrix Algebra
Show steps
-
Revise the concepts of matrices, vectors, and determinants.
-
Practice solving systems of linear equations using matrices.
Attend a workshop on sensor fusion
Show steps
Gain practical insights by attending a workshop that focuses on sensor fusion techniques and applications.
Browse courses on
Sensor Fusion
Show steps
-
Research and identify workshops on sensor fusion that align with your interests.
-
Register and actively participate in the workshop to gain hands-on experience.
Three other activities
Expand to see all activities and additional details
Show all six activities
Simulate particle motion
Show steps
Engage in hands-on practice by simulating particle motion using available software or online tools.
Show steps
-
Select a suitable software or online tool for particle motion simulation.
-
Input initial conditions and parameters for particle motion.
-
Run simulations and analyze the resulting motion patterns.
Explore non-linear optimization techniques
Show steps
Deepen your understanding by exploring non-linear optimization techniques through guided online tutorials or workshops.
Show steps
-
Identify online resources or tutorials on non-linear optimization.
-
Follow the tutorials and work through examples to grasp the concepts.
Develop visual representations
Show steps
Enhance your comprehension by creating visual representations of non-linear motion using graphs, diagrams, or animations.
Show steps
-
Choose appropriate software or tools for creating visual representations.
-
Plot data points and generate graphs to depict non-linear motion.
-
Create diagrams or animations to illustrate the trajectory and behavior of objects in motion.
Connect with experts in non-linear motion
Show steps
Enhance your learning by seeking guidance from experts in the field of non-linear motion.
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or research institutions.
- Reach out to mentors and express your interest in connecting and learning from their expertise.
Review matrix algebra
Show steps
Review the fundamentals of matrix algebra to strengthen your understanding of linear motion.
Browse courses on
Matrix Algebra
Show steps
Show steps
Show steps
- Revise the concepts of matrices, vectors, and determinants.
- Practice solving systems of linear equations using matrices.
Attend a workshop on sensor fusion
Show steps
Gain practical insights by attending a workshop that focuses on sensor fusion techniques and applications.
Browse courses on
Sensor Fusion
Show steps
Show steps
Show steps
- Research and identify workshops on sensor fusion that align with your interests.
- Register and actively participate in the workshop to gain hands-on experience.
Three other activities
Expand to see all activities and additional details
Show all six activities
Simulate particle motion
Show steps
Engage in hands-on practice by simulating particle motion using available software or online tools.
Show steps
Show steps
Show steps
- Select a suitable software or online tool for particle motion simulation.
- Input initial conditions and parameters for particle motion.
- Run simulations and analyze the resulting motion patterns.
Explore non-linear optimization techniques
Show steps
Deepen your understanding by exploring non-linear optimization techniques through guided online tutorials or workshops.
Show steps
Show steps
Show steps
- Identify online resources or tutorials on non-linear optimization.
- Follow the tutorials and work through examples to grasp the concepts.
Develop visual representations
Show steps
Enhance your comprehension by creating visual representations of non-linear motion using graphs, diagrams, or animations.
Show steps
Show steps
Show steps
- Choose appropriate software or tools for creating visual representations.
- Plot data points and generate graphs to depict non-linear motion.
- Create diagrams or animations to illustrate the trajectory and behavior of objects in motion.
Career center
Learners who complete Additional Content: Kalman Filters will develop knowledge and skills
that may be useful to these careers:
Navigation Engineer
Navigation Engineer
Navigation Engineers design and implement systems that guide vehicles from one place to another. They use Unscented Kalman Filters to track the position and orientation of these vehicles, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Navigation Engineer.
Aerospace Engineer
Aerospace Engineer
Aerospace Engineers design, develop, and test aircraft, spacecraft, and other aerospace vehicles. They use Unscented Kalman Filters to track the position and orientation of these vehicles, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as an Aerospace Engineer.
Control Systems Engineer
Control Systems Engineer
Control Systems Engineers design and implement systems that control the behavior of machines and processes. They use Unscented Kalman Filters to estimate the state of these systems, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Control Systems Engineer.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and test robots. They work with mechanical, electrical, and software engineers to create robots that can perform a variety of tasks, from manufacturing to healthcare. Unscented Kalman Filters are used in robotics to track the position and orientation of robots, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Robotics Engineer.
Sensor Fusion Engineer
Sensor Fusion Engineer
Sensor Fusion Engineers design and implement systems that combine data from multiple sensors to create a more accurate and complete picture of the world. They use Unscented Kalman Filters to fuse data from different sensors, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Sensor Fusion Engineer.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. They use Unscented Kalman Filters to estimate the state of systems, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning algorithms. They use Unscented Kalman Filters to improve the performance of machine learning algorithms, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Machine Learning Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software. They use Unscented Kalman Filters to improve the performance of software systems, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Software Engineer.
Test Engineer
Test Engineer
Test Engineers design and conduct tests to ensure that products meet specifications. They use Unscented Kalman Filters to improve the accuracy and reliability of tests, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Test Engineer.
Systems Engineer
Systems Engineer
Systems Engineers design and implement systems that integrate multiple components to achieve a specific goal. They use Unscented Kalman Filters to improve the performance of systems, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Systems Engineer.
Chemical Engineer
Chemical Engineer
Chemical Engineers design and build chemical plants. They use Unscented Kalman Filters to improve the performance of chemical plants, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Chemical Engineer.
Civil Engineer
Civil Engineer
Civil Engineers design and build infrastructure. They use Unscented Kalman Filters to improve the performance of infrastructure, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Civil Engineer.
Electrical Engineer
Electrical Engineer
Electrical Engineers design and build electrical systems. They use Unscented Kalman Filters to improve the performance of electrical systems, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as an Electrical Engineer.
Mechanical Engineer
Mechanical Engineer
Mechanical Engineers design and build machines. They use Unscented Kalman Filters to improve the performance of machines, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as a Mechanical Engineer.
Environmental Engineer
Environmental Engineer
Environmental Engineers design and implement solutions to environmental problems. They use Unscented Kalman Filters to improve the performance of environmental solutions, even in the presence of noise and uncertainty. This course will help you build a foundation in Unscented Kalman Filters, which will be valuable for a career as an Environmental Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/jm30m8/additional
Reading list
We've selected five 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
Additional Content: Kalman Filters.
Provides a comprehensive overview of Gaussian processes, a powerful machine learning technique that can be used for a variety of tasks, including regression, classification, and time series analysis. It valuable resource for anyone who wants to learn more about Gaussian processes and their applications.
Provides a comprehensive overview of Bayesian filtering and smoothing, two powerful techniques for estimating the state of a dynamic system from noisy measurements. It valuable resource for anyone who wants to learn more about Bayesian filtering and smoothing and their applications.
Provides a comprehensive overview of Kalman filtering and time series analysis, two powerful techniques for analyzing and forecasting time series data. It valuable resource for anyone who wants to learn more about Kalman filtering and time series analysis and their applications.
Provides a comprehensive overview of optimal state estimation, a powerful technique for estimating the state of a dynamic system from noisy measurements. It valuable resource for anyone who wants to learn more about optimal state estimation and its applications.
Classic introduction to the Kalman filter, written by the inventor of the Kalman filter himself. It valuable resource for anyone who wants to learn more about the Kalman filter and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jm30m8/additional
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Similar courses
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Time
360 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Explores Unscented Kalman Filters, a technique used for accurate tracking of non-linear motion in sensor fusion
Taught by David Silver and a team of experts from DeepMind, known for their pioneering work in reinforcement learning
Provides a strong foundation for understanding Unscented Kalman Filters and their applications in tracking
Relevant for students and practitioners in robotics, autonomous systems, and state estimation
Requires some background in linear algebra, probability theory, and Kalman filtering
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