Professor of Mechanical Engineering
AI for Autonomous Vehicles and Robotics
What You'll Learn
- Ability to implement machine learning algorithms in autonomous systems
- Learn to design and deploy machine learning models for autonomy
- Application of transfer learning and domain adaptation techniques
3 Modules
6 Hours
2 hrs per module (approx.)
Rating
About AI for Autonomous Vehicles and Robotics
In this course, you will delve into the groundbreaking intersection of AI and autonomous systems, including autonomous vehicles and robotics. “AI for Autonomous Vehicles and Robotics” offers a deep exploration of how machine learning (ML) algorithms and techniques are revolutionizing the field of autonomy, enabling vehicles and robots to perceive, learn, and make decisions in dynamic environments. Through a blend of theoretical insights and practical applications, you’ll gain a solid understanding of supervised and unsupervised learning, reinforcement learning, and deep learning. You will delve into ML techniques tailored for perception tasks, such as object detection, segmentation, and tracking, as well as decision-making and control in autonomous systems. You will also explore advanced topics in machine learning for autonomy, including predictive modeling, transfer learning, and domain adaptation. Real-world applications and case studies will provide insights into how machine learning is powering innovations in self-driving cars, drones, and industrial robots. By the course's end, you will be able to leverage ML techniques to advance autonomy in vehicles and robots, driving innovation and shaping the future of autonomous systems engineering.
Skills You'll Gain
- Autonomous Vehicles
- Generative AI Agents
- Machine Learning
- Robotics
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to AI for Autonomous Vehicles and Robotics, the second course in the AI for Mechanical Engineers series. This course explores how artificial intelligence and machine learning enable autonomy in vehicles and robotic systems. Learners will study perception, decision-making, and control techniques, and examine real-world applications in self-driving cars, drones, and robotics to understand how AI is shaping the future of autonomous systems engineering.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction to Key Concepts and Fundamentals
- Video: Introduction to Robotics Techniques
- Reading: Course Syllabus
- Reading: Help Us Learn About You!
- Reading: Introduction to Jupyter Labs on Coursera
- Video: Introduction to Self-Driving Cars
- Reading: Convolutional Neural Networks
- Graded: Module 1 Assignment
Module 2: Key Algorithms in Robotics and Self-Driving Cars
- Video: Algorithms in Robotics
- Reading: Introduction to Kalman Filters
- Video: Algorithms in Self-Driving Cars
- Reading: Kalman Filters in State Estimation Implementation
- Ungraded Lab: Kalman Filters in State Estimation- Programming Exercise
Module 3: Application of AI/ML in Robotics and Self-Driving Cars
- Video: Motion Planning, Perception, and Learning in Robotics
- Reading: Introduction to Reinforcement Learning
- Reading: Reinforcement Learning (Q-table) Implementation
- Ungraded Lab: Reinforcement Learning (Q-table)- Programming Exercise
- Video: State Estimation and Localization for Autonomous Vehicles
- Reading: Introduction to SLAM
- Video: Visual Perception for Self-Driving Cars
- Reading: Regional Convolutional Neural Networks (R-CNN)
- Reading: End of Course Survey
- Reading: References
- Graded: Module 3 Assignment
Grading Policy
This course consists of three modules, each ending with an auto-graded assignment. To pass each graded assignment, learners must earn a minimum score of 80%. Learners have unlimited attempts, and the highest score is recorded. Graded assignments are required to earn a certificate and are available only to paid learners.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners with general college-level education, industry-relevant experience, and professionals interested in the field are welcome.
