Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
---|---|---|---|---|---|
Advanced Probabılıty Theory For Engıneers | BSM 611 | 0 | 3 + 0 | 3 | 6 |
Ön Koşul Dersleri | |
Önerilen Seçmeli Dersler | |
Dersin Dili | Türkçe |
Dersin Seviyesi | Doktora |
Dersin Türü | Seçmeli |
Dersin Koordinatörü | Prof.Dr. AHMET ÖZMEN |
Dersi Verenler | |
Dersin Yardımcıları | |
Dersin Kategorisi | Diğer |
Dersin Amacı | To teach the student theory and applications of Estimation Theory |
Dersin İçeriği | Introduction, Coverage, Philosophy, and Computation, The Lineer Model, Parameter Estimation, Least_squares Estimation: Batch Processing, Least_squares Estimation: Singular_value Decomposition, Least_squares Estimation: Recursive Processing, Small_sample Properties of Estimators, Large_sample Properties of Estimators, Properties of Least_squares Estimators, Best Linear Unbiased Estimation, Likelihood Function, Maximum_likelihood Estimation, Multivariate Gaussian Random Variables, Mean_squared Estimation of Random Parameters, Maximum a Posterior: Estimation of Random Parameters, Elements of Discrete_time Gauss_Markov Random Sequences, Some Aplications to real world problems such as System Identification, Communications and Control related Problems, Filtering, Smoothing, Prediction |
# | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
---|---|---|---|
1 | Learning both foundations and the applications of the subject | Lecture, Lab / Workshop, Self Study, Problem Solving, Project Based Learning, Question-Answer, Drilland Practice, Group Study, | Testing, Homework, Project / Design, Performance Task, |
Hafta | Ders Konuları | Ön Hazırlık |
---|---|---|
1 | Introduction, Coverage, Philosophy, and Computation, The Lineer Model, Parameter Estimation | |
2 | Least_squares Estimation: Batch Processing | |
3 | Least_squares Estimation: Singular_value Decomposition | |
4 | Least_squares Estimation: Recursive Processing | |
5 | Small_sample Properties of Estimators | |
6 | Large_sample Properties of Estimators | |
7 | Properties of Least_squares Estimators | |
8 | Best Linear Unbiased Estimation | |
9 | Likelihood Function, Maximum_likelihood Estimation | |
10 | Multivariate Gaussian Random Variables | |
11 | Mean_squared Estimation of Random Parameters | |
12 | Maximum a Posterior: Estimation of Random Parameters | |
13 | Elements of Discrete_time Gauss_Markov Random Sequences | |
14 | Some Aplications to real world problems such as System Identification, Communications and Control related Problems, Filtering, Smoothing, Prediction. |
Kaynaklar | |
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Ders Notu | |
Ders Kaynakları |
Sıra | Program Çıktıları | Katkı Düzeyi | |||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
2 | ability to complete and implement “limited or incomplete data” by using the scientific methods. | X | |||||
2 | ability to complete and implement “limited or incomplete data” by using the scientific methods. | X | |||||
3 | ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them | X | |||||
3 | ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them | X | |||||
4 | ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process | X | |||||
4 | ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process | X | |||||
5 | gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering | X | |||||
5 | gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering | X | |||||
6 | ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process | X | |||||
6 | ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process | X | |||||
7 | gain high level ability to define the required information and data | X | |||||
7 | gain high level ability to define the required information and data | X | |||||
8 | ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations | X | |||||
8 | ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations | X | |||||
9 | systematic and clear verbal or written transfer of the process and results of studies at national and international environments | X | |||||
9 | systematic and clear verbal or written transfer of the process and results of studies at national and international environments | X | |||||
10 | aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement | X | |||||
10 | aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement | X | |||||
11 | aware of new and developing application of profession and ability to analyze and study on those applications | X | |||||
11 | aware of new and developing application of profession and ability to analyze and study on those applications | X | |||||
12 | ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment | X | |||||
12 | ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment | X |
Değerlendirme Sistemi | |
---|---|
Yarıyıl Çalışmaları | Katkı Oranı |
1. Ara Sınav | 40 |
1. Kısa Sınav | 20 |
1. Ödev | 20 |
2. Kısa Sınav | 20 |
Toplam | 100 |
1. Yıl İçinin Başarıya | 50 |
1. Final | 50 |
Toplam | 100 |
AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
---|---|---|---|
Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
Mid-terms | 1 | 15 | 15 |
Assignment | 1 | 10 | 10 |
Final examination | 1 | 20 | 20 |
Toplam İş Yükü | 141 | ||
Toplam İş Yükü / 25 (Saat) | 5,64 | ||
Dersin AKTS Kredisi | 6 |