I helped design and eventually taught a semester-long 3rd year engineering science course based on Russel and Norvig's classic textbook and Sutton and Barto's Reinforcement Learning: An Introduction. Lectures, assignments and exams covered: the history of AI, search, logic, constraint programming, game playing AI, ethics of AI, planning, decision tree learning, (partially observable) Markov decision processes, and reinforcement learning.
Python programming assignments included:
Basic search algorithms over graphs and trees
Robot motion planning
A game-playing AI competition
Solving MDPs and -armed bandit problems
An essay on ethical issues surrounding advances in AI systems
Remotely delivered lectures to a class of 77 students during the COVID-19 pandemic. With the help of two TAs, the course's assessments were modified to a format appropriate for remote learning.
Duties included creating Matlab and Python assignments with Autolab, running tutorials, answering students' questions, and creating and grading midterm questions.