STAT576 NEURAL NETWORKS FOR DATA SCIENCE
| Course Code: |
2460576 |
| METU Credit (Theoretical-Laboratory hours/week): |
3(3-0) |
| ECTS Credit: |
8.0 |
| Department: |
Statistics |
| Language of Instruction: |
English |
| Level of Study: |
Graduate |
| Course Coordinator: |
Prof.Dr. ÝNCÝ BATMAZ |
| Offered Semester: |
Fall and Spring Semesters. |
Course Content
Basics of neural network (NN) computing. AI problem solving. and Von Neumann architecture. Important neural network models. Adaline and Perceptron; feedforward. feedback. recurrent and self-organizing and thermodynamic networks. Learning methods. Hebbian. perceptron. back-propagation learning and unsupervised competitive learning. Hopfield Network. Data preprocessing: principal and independent component analysis. Practical applications of these techniques in Data Science.
