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 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

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 Gain sufficient knowledge in mathematics, science and computing; be able to use theoretical and applied knowledge in these areas to solve engineering problems related to information systems.
2 To be able to identify, define, formulate and solve complex engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.
3 Designs a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.
4 To be able to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in information systems engineering applications; to be able to use information technologies effectively. X
5 Designs and conducts experiments, collects data, analyzes and interprets results to investigate complex engineering problems or research topics specific to the discipline of information systems engineering. X
6 Can work effectively in disciplinary and multidisciplinary teams; can work individually.
7 a. Communicates effectively both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. b. Knows at least one foreign language.
8 To be aware of the necessity of lifelong learning; to be able to access information, to be able to follow developments in science and technology and to be able to renew himself/herself continuously.
9 a. Acts in accordance with the principles of ethics, gains awareness of professional and ethical responsibility. b. Gains knowledge about the standards used in information systems engineering applications.
10 a. Gains knowledge about business life practices such as project management, risk management and change management. b. Gains awareness about entrepreneurship and innovation. c. Gains knowledge about sustainable development.
11 a. To be able to acquire knowledge about the universal and social effects of information systems engineering applications on health, environment and safety and the problems of the era reflected in the field of engineering. b. Gains awareness of the legal consequences of engineering solutions.

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