Information Package / Course Catalogue
| Course Code | : MTK638 |
| Course Type | : Area Elective |
| Couse Group | : Third Cycle (Doctorate Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 8 |
Artificial neural networks (ANNs) as the subjects of artificial intelligence are widely used in computer science. ANNs are very advantageous in most systems, especially in the systems which have very complex mathematical structures. In this course, the aim is to teach ANN subjects in detail and to develop advanced ANN applications.
Introduction to Artificial Intelligence and Machine Learning. Introduction to Artificial Neural Networks (ANNs). The basic structures of ANNs. Elementary Artificial Neural Networks. Supervised learning. Multilayer Perceptron. Reinforcement learning. Learning Vector Quantization (LVQ). Unsupervised learning. Adaptive Resonance Theory (ART). Recurrent Neural Networks and other networks. Hybrid ANN Models. Neural Network Hardware. Applications of ANN.
| 1. | Ability to understand the artificial neural network (ANN) concepts |
| 2. | Ability to design ANN methods which use supervised, unsupervised and reinforcement learning approaches |
| 3. | Ability to develop advanced applications using ANN |
| 4. | To be able to gain the skill of interpreting some interrelations among these concepts |
| 5. | To be able to use mathematical concepts in solving certain types of problems |
| 1. | Foundations of Neural Networks, T. Khana, Addison-Wesley Publishing Comp., 1990 |
| 2. | Yapay Sinir Ağları, Prof. Dr. Ercan Öztemel, Papatya Yayıncılık, 2003 |
| Type of Assessment | Count | Percent |
|---|---|---|
| Assignment | 1 | %5 |
| Term 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 | 0 | 6 | 6 |
| Term Project | 1 | 0 | 6 | 6 |
| Individual Work | 14 | 0 | 5 | 70 |
| Midterm Examination | 1 | 30 | 2 | 32 |
| Final Examination | 1 | 42 | 2 | 44 |
| TOTAL WORKLOAD (hours) | 200 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | PÇ-13 | PÇ-14 | PÇ-15 | |
OÇ-1 | 3 | 3 | 3 | 4 | 4 | 3 | 2 | 2 | |||||||
OÇ-2 | 5 | 4 | 5 | 5 | 3 | 4 | 3 | 4 | 4 | 4 | |||||
OÇ-3 | 5 | 5 | 5 | 5 | 5 | 3 | 5 | 4 | 4 | 5 | 2 | 4 | |||
OÇ-4 | 4 | 5 | 4 | 4 | 5 | 3 | 4 | 4 | 3 | 3 | 3 | ||||
OÇ-5 | 4 | 5 | 4 | 4 | 5 | 4 | 3 | 4 | 3 | 3 | 4 | ||||