ECTS - Applied Neural Computing
Applied Neural Computing (CMPE461) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Applied Neural Computing | CMPE461 | Area Elective | 2 | 2 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| MATH275 |
| 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. |
| Course Lecturer(s) |
|
| Course Objectives | This course has the objective to provide an introduction to neural network architectures, learning algorithms, and their applications. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to neural networks, perceptron learning rules, backpropagation algorithms, generalization and overtraining, adaptive linear filters, radial basis networks, self organizing networks, learning vector quantization, recurrent networks. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to neural networks. | Chapter 1 (main text) |
| 2 | Perceptron learning rules | Chapter 5.1-5.3 |
| 3 | Linear, nonlinear, and stochastic units in simple perceptrons and applications | Chapter 5.4-5.7 |
| 4 | Backpropagation | Chapter 6.1 |
| 5 | Variations on backpropagation and applications | Chapter 6.2, 6.3 |
| 6 | Generalization and overtraining | Chapter 6.4-6.6 |
| 7 | Recurrent networks | Chapter 7 |
| 8 | Unsupervised learning | Chapter 8.1-8.3 |
| 9 | Self organizing networks | Chapter 8.4 |
| 10 | Adaptive linear filters | Chapter 9.1-9.4 |
| 11 | Learning vector quantization | Chapter 6.3 (Other sources 2) |
| 12 | Radial basis networks | Chapter 5 (Other sources 1) |
| 13 | Applications of neural networks | Various sources |
| 14 | Applications of neural networks | Various sources |
Sources
| Course Book | 1. Hertz, Krogh, & Palmer (1991) Introduction to the Theory of Neural Computation. Addison-Wesley. |
|---|---|
| Other Sources | 2. 1. Bishop (2005). Neural Networks for Pattern Recognition. Oxford University Press. |
| 3. 2. Ripley, Ripley, & Hjort (1996). Pattern Recognition and Neural Networks. Cambridge University Press. | |
| 4. 3. Haykin (1999). Neural Networks: A Comprehensive Foundation (2nd Edition) Macmillan. | |
| 5. 4. Anderson, & Rosenfeld (1998) Neurocomputing: Foundations of Research, MIT Press, Cambridge. | |
| 6. 5. Mitchell (1997). Machine Learning, McGraw Hill, New York. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 2 | 10 |
| Presentation | - | - |
| Project | 2 | 40 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 6 | 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 | Accumulated knowledge on mathematics, science and mechatronics engineering; ability to apply the theoretical and applied knowledge to model and analyze mechatronics engineering problems. | |||||
| 2 | Ability to identify, define and formulate problems related to the field and to select and apply appropriate analysis and modeling methods to solve these problems. | |||||
| 3 | Ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; ability to apply contemporary design methodologies; 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.) | |||||
| 4 | Ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; ability to use information and communications technologies effectively. | |||||
| 5 | Ability to design and perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | |||||
| 6 | Ability to work effectively on intra-disciplinary and multi-disciplinary teams; 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 including electrical & electronics and computer engineering. | |||||
| 7 | Ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. | |||||
| 8 | 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 | 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, social 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 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 | 4 | 64 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 1 | 16 |
| Presentation/Seminar Prepration | |||
| Project | 2 | 10 | 20 |
| Report | |||
| Homework Assignments | 2 | 4 | 8 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 7 | 7 |
| Prepration of Final Exams/Final Jury | 1 | 10 | 10 |
| Total Workload | 125 | ||