ECTS - Neural Networks and Applications
Neural Networks and Applications (EE423) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Neural Networks and Applications | EE423 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice, Team/Group, Brain Storming. |
| Course Lecturer(s) |
|
| Course Objectives | •Introduce the main fundamental principles and techniques of neural network systems. •Investigate the principal neural network models and applications. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | An introduction to basic neurobiology, the main neural network architectures and learning algorithms, and a number of neural network applications, McCulloch Pitts Neurons, Single Layer Perceptrons, Multi-Layer Perceptrons, Radial Basis Function Networks, Committee Machines, Kohonen Self-Organising Maps, and Learning Vector Quantization |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Neural Networks and their History. Biological Neurons and Neural Networks. Artificial Neurons | Glance this week’s topics from the lecture |
| 2 | Networks of Artificial Neurons. Single Layer Perceptrons. Learning and Generalization in Single Layer Perceptrons | Glance this week’s topics from the lecture |
| 3 | Hebbian Learning. Gradient Descent Learning | Glance this week’s topics from the lecture |
| 4 | The Generalized Delta Rule. Practical Considerations | Glance this week’s topics from the lecture |
| 5 | Learning in Multi-Layer Perceptrons. Back-Propagation Algorithms | Glance this week’s topics from the lecture |
| 6 | Learning with Momentum. Conjugate Gradient Learning | Review last week and glance this week’s topics from the lecture |
| 7 | Bias and Variance. Under-Fitting and Over-Fitting. Improving Generalization | Review last week and glance this week’s topics from the lecture |
| 8 | Applications of Multi-Layer Perceptrons | Glance this week’s topics from the lecture |
| 9 | Radial Basis Function Networks: Introduction, Algorithms, and Applications | Glance this week’s topics from the lecture |
| 10 | Associative learning | Glance this week’s topics from the lecture |
| 11 | Competitive networks, Counterpropagation networks, Grossberg networks | Glance this week’s topics from the lecture |
| 12 | Adaptive resonance theory, stability | Glance this week’s topics from the lecture |
| 13 | Hopfield networks, bidirectional associative memories | Glance this week’s topics from the lecture |
| 14 | Self Organizing Maps: Fundamentals, Algorithms, and Applications | Glance this week’s topics from the lecture |
| 15 | Final examination period | Review topics |
| 16 | Fimal examination period | Review topics |
Sources
| Course Book | 1. Neural Networks: A Comprehensive Foundation, Simon Haykin, Pearson Education Inc. Leicestershire U.K 1999 |
|---|---|
| Other Sources | 2. Neural Networks for Pattern Recognition, C. Bishop, Oxford University Press, 1995 |
| 3. Principles of Neurocomputing for Science and Engineering, F.M.Ham and I.Kostanic, McGraw Hill, 2001 |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 15 | 20 |
| Presentation | - | - |
| Project | 1 | 20 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 30 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 19 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | ||||
| 2 | Ability to formulate, and solve complex mechatronics engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | ||||
| 3 | Ability to design a complex mechatronics engineering system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | ||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in mechatronics engineering and robot technology practices; ability to employ information technologies effectively. | X | ||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex mechatronics engineering and robot technology problems or research questions. | X | ||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | ||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself | |||||
| 9 | a-) Knowledge on behavior according to ethical principles, professional and ethical responsibility b-) Knowledge on standards used in engineering practices. | |||||
| 10 | a-) Knowledge about business life practices such as project management, risk management, and change management b-) Awareness in entrepreneurship, innovation; knowledge about sustainable development. | |||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | |||||
| 12 | Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions in the field of mechatronics engineering. | |||||
| 13 | Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planning, improving or changing the norms with a criticism. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | 4 | 5 | 20 |
| Report | |||
| Homework Assignments | 8 | 2 | 16 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 3 | 6 |
| Prepration of Final Exams/Final Jury | 1 | 3 | 3 |
| Total Workload | 125 | ||