AI for Autonomous Vehicles and Robotics

Overview

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.

Syllabus

Introduction to Key Concepts and Fundamentals

In the first module, we describe several types of robotics and explain key technologies for self-driving cars. We will also explain the application of AI in autonomous systems.

Key Algorithms in Robotics and Self-Driving Cars

In Module 2, we will review various types of algorithms that are used in robotics and self-driving cars and explain in more detail the principles and functions of key algorithms. We will also examine the applications of algorithms such as reinforcement learning and object detection techniques.

Application of AI/ML in Robotics and Self-Driving Cars

In the third Module, we will discuss the following concepts related to robotics: motion planning, perception, and learning. For self-driving cars, we will examine state estimation, localization, and visual perception. Finally, we review the applications of key algorithms such as object detection techniques.