AMATH 646Introduction to Mathematics of Deep Learning
- Units
- 0.50
- Level
- Graduate
- Faculty
- Mathematics
- Subject
- Applied Mathematics
AMATH 646 as listed in the Spring 2026 Graduate Calendar. The main page has the current version, when it runs, and seats.
This course introduces the basic concepts of deep learning in a mathematically rigorous fashion. Topics include mathematical definitions of deep neural networks, approximation theory, gradient-based optimization, and generalization analysis. (Held with AMATH 446. Students in AMATH 646 will be expected to meet some additional learning objectives.)
Prerequisites
None listed.
Antirequisites
- Not completed nor concurrently enrolled in:
Leads to
No course lists it as a requisite.
Check it elsewhere for Spring 2026
- Graduate Calendar, Spring 2026The archived entry
- Requirements for Spring 2026Waterloo’s requisite checker
- Schedule of Classes, Spring 2026Instructors, rooms, live seats
- Quest Class SearchWhere you enrol
- Course outlinesSyllabi from past terms (UW login)
- UW FlowStudent ratings and reviews
- WClassesPast sections back to 2004, and who taught them
Calendar versions
Graduate Calendar
- Winter 2026
- Spring 2026 (shown here)
- Fall 2026
- Winter 2024 and earlier in the calendar archives