FACULTY OF ENGINEERING

Department of Industrial Engineering

CE 470 | Course Introduction and Application Information

Course Name
Introduction to Neural Networks
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 470
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Case Study
Lecture / Presentation
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives This course will introduce the fundamental principles and algorithms of Artificial Neural Network (ANN) systems. The course will cover many subjects including basic neuron model, simple perceptron, adaptive linear element, Least Mean Square (LMS) algorithm, Multi Layer Perceptron (MLP), Back Propagation (BP) learning algorithm, Radial Basis Function (RBF) networks, Self Organizing Maps (SOM) and Learning Vector Quantization (LVQ), Support Vector Machines (SVMs), Continuous time and discrete time Hopfield networks, classification techniques, pattern recognition, signal processing and control applications.
Learning Outcomes The students who succeeded in this course;
  • Describe basic artificial neural network models,
  • Use the most common ANN architectures and their learning algorithms for a specific application,
  • Explain the principles of supervised and unsupervised learning, and generalization ability,
  • Evaluate the practical considerations in applying ANNs to real classification, pattern recognition, signal processing and control problems,
  • Implement basic ANN models and algorithms using Matlab and its Neural Network Toolbox.
Course Description The following topics will be included in the course: The main neural network architectures and learning algorithms, perceptrons and the LMS algorithm, back propagation learning, radial basis function networks, support vector machines, Kohonen’s self organizing feature maps, Hopfield networks, artificial neural networks for signal processing, pattern recognition and control.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Biological motivation. Historical remarks on artificial neural networks. Applications of artificial neural networks. A taxonomy of artificial neural network models and learning algorithms. Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
2 General artificial neuron model. Discretevalued perceptron model, threshold logic and their limitations. Discretetime (dynamical) Hopfield networks. Hebb’s rule. Connection wieght matrix as an outer product of memory patterns. Chapter 1. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
3 Supervised learning. Perceptron learning algorithm. Adaptive linear element. Supervised learning as output error minimization problem. Gradient descent algorithm for minimization. Least mean square rule. Chapter 2. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
4 Single layer, continuous valued perceptron. Nonlinear (sigmoidal) activation function. Delta rule. Batch mode and pattern mode gradient descent algorithms. Convergence conditions for deterministic and stochastic gradient descent algorithms. Chapter 3. Chapter 4: Sections 4.1, 4.2, 4.16. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
5 Multi layer perceptron as universal approximator. Function representation and approximation problems. Backpropagation Learning. Local minima problem. Overtraining. Chapter 4: Sections 4.4, 4.5, 4.8, 4.10, 4.12. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
6 Midterm Exam I.
7 Batch and pattern mode training. Training set versus test set. Overfitting problem. General practices for network training and testing. Signal processing and pattern recognition applications of multilayer perceptrons. Chapter 4: Sections 4.3, 4.10., 4.11, 4.13, 4.14, 4.15, 4.19, 4.20. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761.
8 Radial Basis Function (RBF) network. Backpropagation learning for determining linear weights, centers and widths parameters of RBF networks. Random selection of centers. Input versus input-output clustering for center and width determination. Regularization theory, mixture of Gaussian (conditional probability density function) model and neurofuzzy connections of RBF networks. Chapter 5. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761
9 Parametric versus nonparametric methods for data representation. Unsupervised learning as a vector quantization problem. Competitive networks. Winner takes all networks. Kohonen’s self organizing feature map. Clustering. Chapter 9. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761.
10 Signal processing applications of artificial neural networks. Principal component analysis. Data compression and reduction. Image and 1D signal compression and transformation applications of artificial neural networks. Chapter 8. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761
11 Midterm Exam II.
12 Pattern recognition applications of artificial neural networks. Artificial neural networks for feature extraction. Nonlinear feature mapping. Data fusion. Artificial neural networks as classifiers. Image and speech recognition applications. Sections 1.4,1.5., 3.11, 4.7, 5.8, 6.7, S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
13 Implementation of artificial neural networks models and associated learning algorithms for signal processing, pattern recognition and control in MATLAB numerical software environment. L. Fausett, Fundamentals of Neural Networks, Chapter 6, Prentice Hall, ISBN-13: 978-0133341867
14 Cumulative review of artificial neural networks models, learning algorithms and their applications. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
15 Semester Review
16 Final Exam

 

Course Notes/Textbooks S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761
Suggested Readings/Materials

J. M. Zurada, Int. To Artificial Neural Systems, West Publishing Company, 1992 ISBN 053495460X, 9780534954604.

L. Fausett, Fundamentals of Neural Networks, Prentice Hall, ISBN-13: 978-0133341867

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
20
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exams
Midterm
2
50
Final Exam
Total

Weighting of Semester Activities on the Final Grade
100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
2
3
6
Presentation / Jury
0
Project
1
24
24
Seminar / Workshop
0
Oral Exam
0
Midterms
2
15
30
Final Exam
0
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques.

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

X
9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

X
10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

X
12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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