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 | Technical 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 | |
|---|---|
| Major Area Courses | X |
| 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 | Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. | X | ||||
| 2 | An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. | X | ||||
| 3 | An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.) | X | ||||
| 4 | An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. | X | ||||
| 5 | An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | X | ||||
| 6 | An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines. | X | ||||
| 7 | An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. | |||||
| 8 | An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation. | |||||
| 9 | Consciousness on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. | |||||
| 10 | A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project. | |||||
| 11 | Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results 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. | |||||
| 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 planing, 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 | ||