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 | Natural & Applied Sciences Master's Degree |
| 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 |
| 15 | Review | |
| 16 | Review |
Sources
| Course Book | 1. Hertz, Krogh, & Palmer (1991) Introduction to the Theory of Neural Computation. Addison-Wesley. |
|---|---|
| Other Sources | 2. Bishop (2005). Neural Networks for Pattern Recognition. Oxford University Press. |
| 3. Ripley, Ripley, & Hjort (1996). Pattern Recognition and Neural Networks. Cambridge University Press. | |
| 4. Haykin (1999). Neural Networks: A Comprehensive Foundation (2nd Edition) Macmillan. | |
| 5. Anderson, & Rosenfeld (1998) Neurocomputing: Foundations of Research, MIT Press, Cambridge. | |
| 6. 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 | An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. | |||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | |||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | |||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | |||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
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 | ||