ACADEMICS
Course Details
ELE 785 Neural Networks
2018-2019 Spring term information
The course is open this term
| Supervisor(s): | Dr. Atila Yżlmaz | |
| Place | Day | Hours |
|---|---|---|
| SS | Wednesday | 09:00 - 11:45 |
Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.
Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://akts.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.
ELE785 - NEURAL NETWORKS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| NEURAL NETWORKS | ELE785 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prerequisite(s) | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Drill and Practice Case Study Problem Solving | |||||
| Instructor (s) | Assoc.Prof. Atila Yżlmaz | |||||
| Course objective | The course objective is to study on comprehension of the learning paradigms and their network realizations together with introducing well known neural network topologies and their associated learning algorithms. The course would more emphasize the signal processing aspects of neural network tools in engineering applications. | |||||
| Learning outcomes |
| |||||
| Course Content | 1. Introduction to neural networks, 2. Fundamental concepts ? neuron models, Mc Culloch Pitts model, Rosenblatt?s perceptron, learning, 3. Regression and optimization: Least square estimation, recursive least square estimation, derivative based optimization, 4. Single layer perceptrons, 5. Multilayer perceptrons, 6. Self organizing systems: Hebbian learning, Kohonen map, 7. Dynamic networks: Time delay neural networks, recurrent neural networks 8. Radial basis networks | |||||
| References | Haykin, S., Neural Networks, A comprehensive Foundation, Prentice Hall, 2nd ed., 1999. Jang, J.S.R., Sun T.S., Mizutani, E., Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997. Lau, C., edt., Neural Networks, Theoretical Foundations and Analysis, IEEE Press, 1992. Cichocki, A., Unbehauen, R. ,Neural Networks for Optimization and Signal Processing, Wiley,1993. Shalkoff, R.J., ?Artificial Neural Networks?, Mc Graw Hill, 1997. Haykin. S., ? Adaptive Filter Theory?, Prentice Hall, 1996. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to neural networks |
| Week 2 | Fundamental concepts ? neuron models, Mc Culloch Pitts model |
| Week 3 | Fundamental concepts ? Knowledge representation, Rosenblatt?s perceptron, learning paradigms |
| Week 4 | Regression and optimization: least square estimation, recursive least square estimation, |
| Week 5 | Regression and optimization: derivative based optimization |
| Week 6 | Single layer perceptrons |
| Week 7 | Multilayer perceptrons: Backpropagation algorithm |
| Week 8 | Multilayer perceptrons: Programming considerations, applications |
| Week 9 | Midterm Exam |
| Week 10 | Self orginizing systems: Hebbian lerning, Kohonen map |
| Week 11 | Dynamic networks: time delay neural networks |
| Week 12 | Dynamic networks: recurrent neural networks |
| Week 13 | Radial basis networks |
| Week 14 | Engineering applications and comparisons |
| Week 15 | Final exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 3 | 45 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 15 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 4 | 60 |
| Percentage of final exam contributing grade succes | 1 | 40 |
| Total | 100 | |
Workload and ECTS calculation
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 8 | 104 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 3 | 25 | 75 |
| Midterms (Study duration) | 1 | 15 | 15 |
| Final Exam (Study duration) | 1 | 25 | 25 |
| Total Workload | 32 | 76 | 261 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering. | X | ||||
| 2. Has knowledge, skills and and competence to develop novel approaches in science and technology. | X | ||||
| 3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research. | X | ||||
| 4. Can independently carry out all stages of a novel research project. | X | ||||
| 5. Designs, plans and manages novel research projects; can lead multidisiplinary projects. | X | ||||
| 6. Contributes to the science and technology literature. | X | ||||
| 7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English. | X | ||||
| 8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest