
| Course Code | : EE465 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
It is aimed to give the theoretical and practical knowledge to the students about artificial neural networks.
Basic Artificial Neural Networks, Statistical Pattern Recognition, Classification, Single Layer Networks, Multilayer Networks - Fault Backward Spreading Models, Radial Based Functions, Error Functions, Supervised learning, Python applications.