Description: This lecture introduces the fundamental concepts of an associative network in neural computation. Studying a simple network architecture allows analyzing the process of associating one memory to another through tuned synaptic connections. The discussion combines mathematical and computational study of this system, setting the foundation for further study in neural networks and machine learning. This course will be 50% lecture and 50% lab. The lab will be hands-on, with students able to work interactively at the computer they use for the Zoom session.
Teachers: Lyle Muller (Western University, OBI Centre for Analytics) and Roberto Budzinski (University of Lethbridge, OBI Centre for Analytics)
Level: Intermediate
Format: Lecture + Hands-on.
Certificates: Attendance and Completion
Prerequisites:
- Basic linear algebra (vectors, matrices, matrix multiplication) and programming (functions, variables, loops).
- Basic Python knowledge and know-how.
NOTE: This course has limited enrolment. If enrolled and you will not be able to attend, then kindly unenrol so another person can enrol.