TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ELECTRICAL-ELECTRONICS ENGINEERING (MASTER) (WITH THESIS) (ENGLISH) | |
| Code | EE589 |
| Name | Introduction to Neural Networks |
| Term | 2018-2019 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. TURGAY İBRİKÇİ |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
This course involves the integration of computers, software tools, and databases in an effort to address biological recently questions in bioinformatics. Bioinformatics approaches are often used for major initiatives that generate large data sets.
Course Content
This course covers to introduce recently bioinformatics problems in the context of history. Developments in biology that lead to bioinformatics, Sequence Comparison, Genome sequencing, Proteomics
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learning the basic concepts of artificial neural networks |
| LO02 | Understanding of NN |
| LO03 | To provide solutions to ANN problems with MATLAB solution |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction-Fundamental Concepts of Intelligence | Internet sources | |
| 2 | Fundamental Concepts of ANNs | Internet sources | |
| 3 | Basic Models and Learning Rules | Internet sources | |
| 4 | Feedforward Neural Networks | Internet sources | |
| 5 | Perceptron Learning Rule | Internet sources | |
| 6 | Multi Layer perceptron / Gradient Descent Algorithm | Internet sources | |
| 7 | MATLAB and ANN | Internet sources | |
| 8 | Mid-Term Exam | ||
| 9 | Unsupervised Learning | Internet sources | |
| 10 | Unsupervised Learning and Self Organizing Maps- SOM | Internet sources | |
| 11 | Evaluations of NN | Internet sources | |
| 12 | Radial Basis Functions-RBF NN | Internet sources | |
| 13 | Support Vector Machines - SVM | Internet sources | |
| 14 | Presentations - I | Internet sources | |
| 15 | Presentations - II | Internet sources | |
| 16 | Term Exams | ||
| 17 | Term Exams |