This is a project-oriented course that focuses on practical techniques to deploy various machine learning frameworks and algorithms on resource constrained embedded systems. Throughout the semester, students will form teams to work on a project induced from real-world problems of interest, including but not limited to natural language processing, autonomous vehicles and mobile/implantable healthcare devices. Students will be able to choose from a wide range of hardware platforms including microcontrollers, mobile CPUs, edge GPUs and/or FPGAs. In addition to gaining project experience, students will also be able to learn about state-of-the-art on trustworthiness and security of edge intelligence, hardware-aware machine learning, hardware and neural architecture co-design, etc. The course is intended for students who are interested in the application of machine learning in real-world problems.