Not offered in the current semester · Last offered AY2025/2026 Semester 1
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.
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | MH2100, MH3500, MH3600, PS0001 |
| Not Available To Programme | |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | |
| Not Offered As BDE | |
| Not Offered As Unrestricted Elective | |
| Exam |
Total hours per week: 8 hrs
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
Other offerings
AY24/25
AY22/23