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Language of Instruction
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English
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Level of Course Unit
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Doctorate's Degree
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Department / Program
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ELECTRICAL AND COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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The purpose of this course is:
1. to deepen the student understanding of the basic principles of statistical mechanics and thermodynamics analysis;
2. to develop the student skills for practical analysis of one- and multi-channel signals in different real-life applications;
3. to develop the student skills for practical analysis of networks in different real-life applications;
4. to improve the student computational skills for statistical mechanics analysis;
5. to improve the student skills for their independent studies of original scientific literature.
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Course Content
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Basic concepts and examples.
Brief introduction to statistical thermodynamics.
Single channel signals and their statistical analysis.
Multi-channel (vector) signals and their statistical analysis.
Review of computer tools for statistical analysis of signals.
Real life applications of signal statistical analysis.
Summary for signals.
Basic concepts of networks.
Statistical mechanics and thermodynamics of networks.
Phase transitions in networks.
Architecture of networks and their statistical mechanics analysis.
Review of computer tools for statistical analysis of networks.
Application of network statistical models to real life.
Review of control methods for network statistical analysis.
Perspectives of statistical mechanics approach for the real life applications.
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Course Methods and Techniques
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Class participation: During the lectures, considerable amount of time will be allocated for active learning exercises. Bonus grades may be rewarded to outstanding students.
Quizzes: There will be three announced capsule quizzes, which will cover the four subcomponents. Additionally, each subcomponent will have a number of separate announced quizzes throughout the term that will cover only that subcomponent’s content. Dates of the individual subcomponent quizzes will be announced separately.
Homework: There will be three homework assignments during the semester.
The homework material will be announced through Canvas. The first page of the submission should include a cover page (the Cover Page example is available on Canvas). The deadlines of the homework will be announced on Canvas.
Late submissions: A late submission may be submitted within 48 hours of the due date, with a penalty of 40% reduction in total grade received.
Recitations: There will be recitation hours which will be announced beforehand in class or on Canvas.
Exams: There will be one final examination. The exam may include qualitative/quantitative problems, short answer questions, multiple choice questions. A calculator may be needed for the exams. You may not use a smart phone or other network device for this purpose.
Attendance policy: You are strongly recommended to follow the classes in schedule. Attendance is a crucial part of the course as lecture materials are covered during the lectures using the active learning tools, which requires in-class engagement. Regular quizzes are a good measure for attendance. Anyone fails to meet 70% attendance will fail from the course.
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Prerequisites and co-requisities
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None
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Course Coordinator
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Associate Prof.Dr. SERGEY BORISENOK sergey.borisenok@agu.edu.tr
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Name of Lecturers
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None
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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M. Potters, W. Bialek, “Statistical mechanics and visual signal processing”, Journal de Physique I, EDP Sciences, Vol. 4 (11), pp.1755-1775 (1994).
D. Wang, “Application of statistical physics in time series analysis”, Nanjing University (2007).
W. Kinzel, “Statistical physics of neural networks”, Computer Physics Communications, Vol. 121–122, pp. 86–93 (1999).
Ch. H. Yeung, D. Saad, “Networking - A Statistical Physics Perspective”, Journal of Physics A: Mathematical and Theoretical, Vol. 46, p. 103001 (2013).
R. Albert, A.-L. Barabasi, “Statistical mechanics of complex networks”, Reviews of Modern Physics, Vol. 74, p. 47 (2002).
J. Park and M. E. J. Newman, “Statistical mechanics of networks”, Physical Review E, Vol. 70, p. 066117 (2004).
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Course Category
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Mathematics and Basic Sciences
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Engineering
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Engineering Design
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Social Sciences
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Education
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Science
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Health
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Field
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