Artificial Neural Networks
Information Systems Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Artificial Neural Networks | BTM510 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Prof. Dr. Mehmet Melih İNAL
Research Assistant Seda BALTA
Research Assistant Gizem YILDIZ
Research Assistant M.M. Enes YURTSEVER
Learning Outcomes of the Course Unit
1) Artificial neural networks (ANN) learning process.
2) Feedforward ANN. Feedback neural networks.
3) Principal components analysis.
4) Neurodinamic programming.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Learning Outcomes | ||||||||
| 1 | No relation | No relation | No relation | Middle | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Advanced Programming with Matlab and Simulink
Course Contents
Learning in neural networks. Application areas of neural networks. First neural networks. Feed forward neural networks. Recurrent neural networks. Radial basis neural networks. Associative memory networks. Principal components analyses. Self-organizing maps neural networks. Learning vector quantization networks. Adaptive resonance theory networks. Module neural networks. Neural dynamic programming.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Not Required