Chevron Left
返回到 AI for Autonomous Vehicles and Robotics

学生对 University of Michigan 提供的 AI for Autonomous Vehicles and Robotics 的评价和反馈

4.4
38 个评分

课程概述

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....
筛选依据:

1 - AI for Autonomous Vehicles and Robotics 的 6 个评论(共 6 个)

创建者 Aryan C

Jul 2, 2025

very vague and uninspiring...prof seems to be uninterested in the material he's teaching and simply reading off the slides

创建者 Saptajit B

Apr 17, 2025

Notes provided in this course are very good

创建者 YELLATURI M

Jul 28, 2025

GOOD AND EXCELLENT AND VERY VERY GOOD

创建者 Muhammad T

Feb 15, 2025

Amazing Course

创建者 TAHIROU D M

Jul 18, 2025

super cool

创建者 Yecheng W

Aug 5, 2025

Generally good. From Module 1 to 3, it is gradually getting more difficult and requiring more knowledge and concentration. Maybe it can be more technical oriental rather than conceptional.