ACADEMICS
Course Details
ELE 685 Neural Networks
2019-2020 Fall term information
The course is not open this term
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.
ELE685 - NEURAL NETWORKS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| NEURAL NETWORKS | ELE685 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prerequisite(s) | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Case Study Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| 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 | 4 | 52 |
| 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 | 72 | 209 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge. | X | ||||
| 2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest