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Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
ADVANCED PROBABILITY THEORY FOR ENGINEERS BSM 611 0 3 + 0 3 6
Ön Koşul Dersleri
Önerilen Seçmeli Dersler
Dersin Dili Türkçe
Dersin Seviyesi Doktora
Dersin Türü SECMELI
Dersin Koordinatörü Prof.Dr. AHMET ÖZMEN
Dersi Verenler
Dersin Yardımcıları
Dersin Kategorisi
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
Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri
1 - Learning both foundations and the applications of the subject 1 - 13 - 14 - 15 - 16 - 2 - 4 - 8 - A - C - D - F -
Öğretim Yöntemleri: 1:Lecture 13:Lab / Workshop 14:Self Study 15:Problem Solving 16:Project Based Learning 2:Question-Answer 4:Drilland Practice 8:Group Study
Ölçme Yöntemleri: A:Testing C:Homework D:Project / Design F:Performance Task

Ders Akışı

Hafta Konular ÖnHazırlık
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

Ders Notu
Ders Kaynakları

Döküman Paylaşımı


Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
2 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
3 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
4 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
5 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 gain high level ability to define the required information and data X
7 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
8 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
9 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 new and developing application of profession and ability to analyze and study on those applications X
11 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X
12 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
13 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
14 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
15 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
16 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
17 gain high level ability to define the required information and data X
18 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
19 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
20 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
21 aware of new and developing application of profession and ability to analyze and study on those applications X
22 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 40
KisaSinav 1 20
Odev 1 20
KisaSinav 2 20
Toplam 100
Yıliçinin Başarıya Oranı 50
Finalin Başarıya Oranı 50
Toplam 100

AKTS - İş Yükü

Etkinlik Sayısı Süresi(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(s) 5.64
Dersin AKTS Kredisi 5.64
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