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This course covers the basic principles of reinforcement learning and popular modern reinforcement learning algorithms. Students will develop familiarity with both model-based and model-free reinforcement learning algorithms, including Q-learning, Actor-Critic algorithms, and multi-armed bandit algorithms.
Job Responsibility
Assisting in course planning and the delivery of UMSI courses on Coursera
Grading student assignments and exams
Conducting office hours and open lab times to meet with students
Attending weekly staff meetings
Managing autograders
Assisting students as they work on assignments
Facilitating small group online discussions and student conversations
Demonstrating respect for students as individuals and fostering a respectful atmosphere in the online learning environment
Working collaboratively with lead instructors and other instructional team members
Requirements
UM Graduate student in good standing
Must meet eligibility criteria as defined in the GEO contract
Must be lawfully able to be employed in the United States, sponsorship to obtain such status is not available at this time
Programming: proficiency in Python
Jupyter Notebooks
PyTorch, OpenAI.Gym
Mathematics: linear algebra, probability and statistics, dynamic programming, reinforcement learning theory, and deep reinforcement learning algorithms
Experiment Design: Familiar with implementation of deep reinforcement learning algorithms, including DQN, DDQN, DDPG, SAC
Familiar with the OpenAI.Gym environment
Nice to have
Experience or interest in teaching
Strong communication and analytical skills
Experience teaching programming and technology skills to beginning students