These are the courses that I regularly teach; CSCE 580: AI or CSCE 212: Computer Architecture in Spring and CSCE 585: ML Systems in Fall semesters.
This undergraduate course will introduce the basic ideas and techniques underlying the design of intelligent computer-based systems. In this course, a specific emphasis will be on statistical inference and machine learning. Students learn how to build real AI systems that make decisions and act in fully informed, partially observable, or adversarial environments.
There is a lot more to AI/ML than just implementing an algorithm or a technique. In this graduate course, students will work on deploying machine learning systems into production. By the end of the course, students will be able to apply deep learning, in whatever problem you are interested in, at scale and learn how to deal with unique challenges that only may happen when building production-ready AI/ML Systems.
The course provides a first introduction to computer architecture. It covers technical foundations of how a computing platform is designed from the bottom up. The focus is on fundamental techniques employed in the design of modern microprocessors and their hardware/software interface.