Information Package / Course Catalogue
| Course Code | : BİS525 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 3 |
Students will make familiar with the basics of artificial neural networks methods and with their applicability to various problems including time series, regression, clustering, classification and dimension reduction.
Theory and application of regression, classification, time series and data reduction.
| 1. | To be able to define basic neural network models |
| 2. | To be able to use commonly used ANN models and learning algorithms for a specific application |
| 3. | To learn the principles of generalization ability with supervized and unsupervized learning |
| 4. | To be able to learn the advantages and limitations of ANN |
| 5. | To be able to make applications about classification and regression problems by using artificial neural networks |
| Type of Assessment | Count | Percent |
|---|---|---|
| Attending Lectures | 1 | %5 |
| Assignment | 1 | %5 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 3 | 42 |
| Assignment | 1 | 1 | 0 | 1 |
| Seminar | 1 | 1 | 2 | 3 |
| Individual Work | 2 | 0 | 2 | 4 |
| Quiz | 2 | 1 | 1 | 4 |
| Midterm Examination | 1 | 10 | 2 | 12 |
| Final Examination | 1 | 10 | 2 | 12 |
| TOTAL WORKLOAD (hours) | 78 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | |
OÇ-1 | 3 | 5 | 4 | 4 | 5 | 4 | 3 | 4 | 5 | 4 |
OÇ-2 | ||||||||||
OÇ-3 | ||||||||||
OÇ-4 | 3 | 4 | 4 | 5 | 5 | 5 | 5 | 4 | 5 | 4 |
OÇ-5 | ||||||||||