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| BILL7211 | Soft Computing | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course intends to teach the students for the principles of the soft computing, and to gain the ability to use the popular methods in this area. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Understand the basic concepts of soft computing. | 1,3,8,10 | 1 | | PO - 2 : | Design and implement soft computing methods. | 1,3,8,10 | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Introduction to Soft Computing, Artificial Neural Networks, Fuzzy Systems, Evolutionary Algorithms, Hybrid Systems, Support Vector Machines, Probabilistic Reasoning. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction: What is Soft Computing, Soft Computing Techniques, Types of Problems, Modeling the Problem, Hazards of Soft Computing | | | Week 2 | Artificial Neural Networks: Artificial Neuron, Multilayer Perceptron, Training, Issues in ANN, Types of ANN | | | Week 3 | RBF network, Learning Vector Quantization, Self-Organizing Maps, Recurrent Neural Networks, Hopfield Neural Networks | | | Week 4 | Fuzzy Systems: Fuzzy Logic, Membership Functions, Fuzzy Logical Operators, More Operations | | | Week 5 | Fuzzy Inference Systems, Type-2 Fuzzy Systems, Other Sets, Fuzzy Control, Fuzzy Clustering | | | Week 6 | Evolutionary Algorithms: Genetic Algorithms | | | Week 7 | Fitness Scaling, Selection, Mutation, Crossover | | | Week 8 | Other Genetic Operators, Convergence, Diversity, Grammatical Evolution | | | Week 9 | Mid-term Exam | | | Week 10 | Particle Swarm Optimization, Ant Colony Optimization, Metaheuristic Search, Traveling Salesman Problem | | | Week 11 | Hybrid Systems: Evolutionary Neural Networks, Evolving Fuzzy Logic, Fuzzy ANN, Modular Neural Networks | | | Week 12 | Neuro Fuzzy Systems: Cooperative Neuro Fuzzy Systems, Neural Network-Driven Fuzzy Reasoning, Hybrid Neuro-Fuzzy Systems, Construction of Neuro-Fuzzy Systems | | | Week 13 | Support Vector Machines: Risk Minimization Principle, VC Dimension, Structural Risk Minimization, Linear Soft Margin Classifier, The Nonlinear SVM | | | Week 14 | Probabilistic Reasoning: Bayes Networks, Elements of Probability and Graph Theory, Decompositions, Evidence Propagations, Learning Graphical Models | | | Week 15 | Term Project | | | Week 16 | Final Exam | | | |
| 1 | Shukla, A., Tiwari, R. ve Kala, R., Real Life Applications of Soft Computing, CRC Press, 2010, 686 sayfa. | | | |
| 1 | Karray, F. O. ve De Silva, C. W., Soft Computing and Intelligent Systems Design: Theory, Tools and Applications, Addison Wesley, 2004, 584 sayfa. | | | 2 | Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M. ve Held, P., Computational Intelligence: A Methodological Introduction, Springer, 2013, 492 sayfa. | | | 3 | Kecman, V., Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, A Bradford Book, 2001, 608 sayfa. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Project | 15 | | 1 | 20 | | End-of-term exam | 16 | | 3 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 12 | 1 | 12 | | Arasınav | 2 | 1 | 2 | | Proje | 5 | 14 | 70 | | Dönem sonu sınavı için hazırlık | 15 | 1 | 15 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 200 |
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