dm.ieu.edu.tr
Course Name | |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
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Fall/Spring |
Prerequisites | None | |||||
Course Language | ||||||
Course Type | Elective | |||||
Course Level | - | |||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Problem SolvingCase Study | |||||
Course Coordinator | ||||||
Course Lecturer(s) | ||||||
Assistant(s) | - |
Course Objectives | |
Learning Outcomes | The students who succeeded in this course;
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Course Description |
| Core Courses | |
Major Area Courses | ||
Supportive Courses | X | |
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
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. 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. Lecture Notes. |
7 | Radial Basis Function (RBF) network. Backpropagation learning for determining linear weights, centers and widths parameters of RBF networks. Random selection of centers. Input versus inputoutput clustering for center and width determination. Regularization theory, mixture of Gaussion (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 Lecture Notes. |
8 | Support vector machines for classification. Kernel representations. Generalization ability. VapnikChervonenkis dimension. Support vector regression. Comparison of different kernels, loss (error) functions and norms for (separating hyerplane) flatness. | Chapter 6. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761 Lecture Notes. |
9 | Parametric versus nonparametric methods for data representation. Unsupervised learning as a vector quantization problem. Competetive networks. Winner take all network. Kohonen’s selforganizing feature map. Clustering. | Chapter 9. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
10 | Continuous time Hopfield networks. Stability analysis of multiple equilibria of Hopfield networks. Hopfield networks for cost minimization: Lyapunov (energy) based design of Hopfield networks. Associative memory. Traveling salesman problem. Combinatorial optimization. | Chapter 13: Sections 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
11 | Midterm Exam II. Signal processing applications of artificial neural networks. Principle 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 |
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 | Control applications of artificial neural networks. Artificial neural networks for system identification. Artificial neural networks as controllers. Inverse systems design. Direct and indirect control methods. Adaptive control applications. | Chapter 15: Section 15.3. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes. |
14 | Implementation of artificial neural networks models and associated learning algorithms for signal processing, pattern recognition and control in MATLAB numerical software environment. | Lecture Notes. |
15 | 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. |
16 | Review of the Semester |
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. |
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 Exam | ||
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 |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 15 | 1 | |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | 5 | 3 | |
Presentation / Jury | |||
Project | 1 | 24 | |
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 2 | 9 | |
Final Exams | |||
Total | 120 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have a grasp of basic mathematics, applied mathematics and theories and applications of statistics. | |||||
2 | To be able to use theoretical and applied knowledge acquired in the advanced fields of mathematics and statistics, | |||||
3 | To be able to define and analyze problems and to find solutions based on scientific methods, | |||||
4 | To be able to apply mathematics and statistics in real life with interdisciplinary approach and to discover their potentials, | X | ||||
5 | To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself, | X | ||||
6 | To be able to criticize and renew her/his own models and solutions, | |||||
7 | To be able to tell theoretical and technical information easily to both experts in detail and nonexperts in basic and comprehensible way, | |||||
8 | To be able to use international resources in English and in a second foreign language from the European Language Portfolio (at the level of B1) effectively and to keep knowledge up-to-date, to communicate comfortably with colleagues from Turkey and other countries, to follow periodic literature, | |||||
9 | To be familiar with computer programs used in the fields of mathematics and statistics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level, | X | ||||
10 | To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement, | |||||
11 | To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense, | |||||
12 | By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere, | |||||
13 | To be able to continue lifelong learning by renewing the knowledge, the abilities and the compentencies which have been developed during the program, and being conscious about lifelong learning, | |||||
14 | To be able to adapt and transfer the knowledge gained in the areas of mathematics and statistics to the level of secondary school, | |||||
15 | To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest