This course investigates deep learning from the perspectives of several mathematical theories: numerical optimisation, statistical learning, function approximation, and coding theory. The aim is to shed some light on why and under what circumstances deep learning can be expected to work well - or not.
Total hours per week: 4 hrs
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | MH2100, MH3500, MH3600, PS0001 |
| Exam | 3 December 2026, 9.00 am - 11.00 am |
The Exam information shown may be subject to changes. Students are to check the finalised exam timetable with exam seat information, which will be available at the 'Examination Seating Arrangement' webpage, 2 weeks before start of examination.
Prerequisite Graph
Required first
MH2100Calculus IIIMH3500StatisticsMH3600Topology & ManifoldsPS0001Introduction To Computational ThinkingMH3520
Mathematics Of Deep Learning
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Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 1030 | COMMON LEC (LE) 1030-1220 Thu SPMS-LT5 | ||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | COMMON LEC (LE) 1530-1620 Fri SPMS-LT5 | ||||
| 1600 | |||||
| 1630 | 70286 TUT (T) 1630-1720 Fri SPMS-LT5 | ||||
| 1700 |
Other offerings
AY25/26
AY24/25
AY22/23