AI for Autonomous Vehicles and Robotics
Machine learning revolutionizes autonomous systems using neural networks and reinforcement learning.
Machine learning revolutionizes autonomous systems using neural networks and reinforcement learning.
This comprehensive course examines the intersection of artificial intelligence and autonomous systems, focusing on applications in robotics and self-driving vehicles. Students will explore how machine learning algorithms enable autonomous systems to perceive their environment, make decisions, and learn from experience. The curriculum begins with fundamental concepts in robotics and autonomous vehicle technology, establishing a foundation for understanding the role of AI in these fields. The second module delves into key algorithms used in robotics and self-driving cars, including reinforcement learning, Kalman filters for state estimation, and object detection techniques. In the final module, students explore advanced topics such as motion planning, perception, and learning in robotics, as well as state estimation and visual perception for autonomous vehicles. Through a combination of theoretical concepts and practical programming exercises, students gain hands-on experience implementing algorithms like reinforcement learning and Kalman filters. By the end of the course, participants will understand how to leverage AI techniques to advance autonomy in vehicles and robots, driving innovation in this rapidly evolving field.
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What you'll learn
Understand fundamental concepts of robotics and autonomous vehicles
Implement key algorithms like Kalman filters for state estimation
Apply reinforcement learning techniques to autonomous systems
Develop motion planning strategies for robots and vehicles
Utilize perception algorithms for environmental awareness
Implement visual perception systems using deep learning
Skills you'll gain
This course includes:
1 Hours PreRecorded video
3 assignments
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FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to artificial intelligence applications in autonomous vehicles and robotics. The curriculum is structured in three progressive modules that build from fundamental concepts to practical implementations. Students begin by exploring the foundations of robotics techniques and autonomous vehicle technologies, with emphasis on how AI enhances these systems' capabilities. The second module focuses on key algorithms essential for autonomous operation, including reinforcement learning, Kalman filters for state estimation, and various perception techniques. Students gain hands-on experience implementing these algorithms through programming exercises. The final module explores advanced applications in both robotics and self-driving cars, covering motion planning, perception systems, simultaneous localization and mapping (SLAM), and visual perception using deep learning. Throughout the course, practical examples demonstrate how these technologies are implemented in real-world autonomous systems. By combining theoretical knowledge with practical programming exercises, students develop the skills needed to understand and contribute to the rapidly evolving fields of autonomous vehicles and robotics.
Introduction to Key Concepts and Fundamentals
Module 1 · 1 Hours to complete
Key Algorithms in Robotics and Self-Driving Cars
Module 2 · 2 Hours to complete
Application of AI/ML in Robotics and Self-Driving Cars
Module 3 · 3 Hours to complete
Fee Structure
Instructor
Professor of Mechanical Engineering
Wei Lu is a Professor in the Department of Mechanical Engineering at the University of Michigan - Ann Arbor. He earned his B.S. from Tsinghua University and his Ph.D. from Princeton University. Prof. Lu specializes in applying machine learning to solve critical challenges in mechanical engineering and energy applications. With over 180 publications in high-impact journals and 200 presentations at prestigious institutions like Harvard, MIT, and Stanford, he is a recognized leader in his field. Prof. Lu's research spans a wide range of topics, integrating artificial intelligence with engineering solutions. His works have been featured in prominent journals such as Nature Communications, Applied Energy, and Journal of Power Sources, showcasing his contributions to both specialized and interdisciplinary audiences.
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