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)
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.
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
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 Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively. X
5 The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. X
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually
7 Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. X
8 The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
9 Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology.
10 The ability to behave according to ethical principles, awareness of professional and ethical responsibility;
11 Knowledge of the standards utilized in software engineering applications
12 Knowledge on business practices such as project management, risk management and change management;
13 Awareness about entrepreneurship, innovation
14 Knowledge on sustainable development
15 Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety; X
16 Awareness of the legal consequences of engineering solutions
17 An ability to describe, analyze and design digital computing and representation systems. X
18 An ability to use appropriate computer engineering concepts and programming languages in solving computing problems. X

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