Hyosub teaches the following three courses during the regular academic term at UBC. While the courses are all unique, common themes across them include an emphasis on active learning, developing tangible skills and skills-based assessments, and fostering creative applications of the knowledge gained through final projects.
Taught in Winter Term 1
This course provides hands-on experience with learning to program in Python. The focus is on getting students to think algorithmically about how to effectively process, visualize, and analyze data related to all types of research questions. Students interested in getting actively involved in research at UBC will find this course especially useful, as the skills you will learn and practice throughout the semester are generalizable to many different types of research environments. No prior programming experience is required or expected.
Taught in Winter Term 2
Computational modelling has been central to many recent advances in our fundamental understanding of human cognition, perception, and action. Through a combination of lectures, readings, and hands-on tutorials, this course provides students with an intuitive, yet rigorous, introduction to computational modelling of sensorimotor control and learning. This course emphasizes Bayesian theories and approaches to how the brain makes optimal decisions and actions despite uncertainty about world states. Prior programming experience is required.
Taught in Winter Term 2
This is a graduate-level introduction to core concepts and techniques in computational motor control and motor learning. Each class session is primarily comprised of student-led presentations and discussions. Some of the main topics covered in the course include: motor planning; multisensory integration; optimal feedback control; error-based learning; reinforcement learning; and Bayesian inference.
Note: The first offering of the course will be in Winter Term 2 of the 2025/2026 academic year. Some of the required readings are likely to change before then.