Course Name Code Semester T+U Hours Credit ECTS
Advanced Probability Theory For Engineers BSM 611 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Doctorate Degree
Course Type Optional
Course Coordinator Prof.Dr. AHMET ÖZMEN
Course Lecturers
Course Assistants
Course Category
Course Objective To teach the student theory and applications of Estimation Theory
Course Content 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
# Course Learning Outcomes Teaching Methods Assessment Methods
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,
Week Course Topics Preliminary Preparation
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.
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
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
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 40
1. Kısa Sınav 20
1. Ödev 20
2. Kısa Sınav 20
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
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
Total Workload 141
Total Workload / 25 (Hours) 5.64
dersAKTSKredisi 6