Applied Neural Computing (CMPE461) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Applied Neural Computing CMPE461 2 2 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Describe the concepts and techniques of neural networks
  • Reason about the behavior of neural networks
  • Evaluate which neural network model is appropriate to a particular application
  • Evaluate pros and cons of neural network models
  • Apply neural networks to particular applications
  • Identify steps to take to improve performance of the algorithms
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


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 An ability to apply knowledge of mathematics, science, and engineering.
2 An ability to design and conduct experiments, as well as to analyse and interpret data.
3 An ability to design a system, component, or process to meet desired needs.
4 An ability to function on multi-disciplinary domains.
5 An ability to identify, formulate, and solve engineering problems.
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 Recognition of the need for, and an ability to engage in life-long learning.
9 A knowledge of contemporary issues.
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
11 Skills in project management and recognition of international standards and methodologies
12 An ability to produce engineering products or prototypes that solve real-life problems.
13 Skills that contribute to professional knowledge.
14 An ability to make methodological scientific research.
15 An ability to produce, report and present an original or known scientific body of knowledge.
16 An ability to defend an originally produced idea.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 4 64
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 2 10 20
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