Available to Vector Institute sponsors only.
Date: March 16 - May 4, 2021
Time: Lectures, Tuesdays 2 - 4 PM. Tutorials, TBA
Location: This course will be delivered online via D2L Brightspace, through live lectures and tutorials
Instructors: Pascal Poupart
Fees: $5,000 $2,000
Please read: Terms and Conditions
Reinforcement learning is a powerful paradigm for modeling autonomous and intelligent agents interacting with the environment, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will study agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources.
The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.
List of Topics covered in this course (expected)
With a focus on AI as the design of agents learning from experience to predict and control their environment, topics will include
At the end of this course, you will have gained both knowledge and system building abilities in:
Participants can expect to spend approximately 10-15 hours per week reading and engaging with the material, attending Tutorials, and completing assignments.
Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab funded by the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). He received the B.Sc. in Mathematics and Computer Science at McGill University, Montreal (Canada) in 1998, the M.Sc. in Computer Science at the University of British Columbia, Vancouver (Canada) in 2000 and the Ph.D. in Computer Science at the University of Toronto, Toronto (Canada) in 2005. His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing, Health Informatics, Computational Finance, Telecommunication Networks and Sports Analytics. He is most well known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include probabilistic deep learning, robust machine learning, data efficient reinforcement learning, conversational agents, automated document editing, adaptive satisfiability, sports analytics and knowledge graphs.
Pascal Poupart received a Canada CIFAR AI Chair (2018-2021), a Cheriton Faculty Fellowship (2015-2018), a best student paper honourable mention (SAT-2017), a silver medal at the SAT-2017 competition, a top reviewer award (ICML-2016), a gold medal at the SAT-2016 competition, a best reviewer award (NIPS-2015), an Early Researcher Award from the Ontario Ministry of Research and Innovation (2008), two Google research awards (2007-2008), a best paper award runner up (UAI-2008) and the IAPR best paper award (ICVS-2007). He serves as member of the editorial board of the Journal of Machine Learning Research (JMLR) (2009 - present), guest editor for the Machine Learning Journal (MLJ) (2012 - present) and associate editor of the Journal of Artificial Intelligence Research (JAIR) (2017-2019), He routinely serves as area chair or senior program committee member for NeurIPS, ICML, AISTATS, ICLR, IJCAI, AAAI and UAI. His research collaborators include Microsoft, RBC Borealis AI, Google, Intel, Ford, ProNavigator, SportLogic, Scribendi, Kik Interactive, In the Chat, Slyce, HockeyTech, the Alzheimer Association, the UW-Schlegel Research Institute for Aging, Sunnybrook Health Science Centre and the Toronto Rehabilitation Institute.
All course Units will be released online according to the schedule below, and supplemented by online tutorials. All lectures and tutorials will be recorded, though we encourage you to attend the sessions live where possible.
Week 1: Week of Mar 15, 2021
Week 2: Week of Mar 22, 2021
Week 3: Week of Mar 29, 2021
Week 4: Week of Apr 5, 2021
Week 5: Week of Apr 12, 2021
Week 6: Week of Apr 19, 2021
Week 7: Week of Apr 26, 2021
Week 8: Week of May 3, 2021