TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Associate Professor Ahmet Güngör Pakfiliz
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Gains the ability to solve problems related to linear shift invariant (LSI) systems. 2) Know and use the structure of the random processes. 3) Knows the characterization of random processes and how processes affect signals. 4) Able to do signal modeling. 5) Know and develop the applications on power spectrum/spectral estimation. 6) Know and use the structures of stochastic models (AR, MA, AR-MA). 7) Know and use the structures of adaptive Filters (Wiener, Kalman).
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Discrete-Time Random Processes. Wiener Filtering. Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). Tracking of Time-Varying Systems.
COURSE CONTENTS	WEEK TOPICS 1st Week Discrete-Time Random Processes. 2nd Week Discrete-Time Random Processes. 3rd Week Wiener Filtering. 4th Week Wiener Filtering. 5th Week Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). 6th Week Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). 7th Week Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). 8th Week Midterm Exam 9th Week Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). 10th Week Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). 11th Week Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). 12th Week Tracking of Time-Varying Systems. 13th Week Tracking of Time-Varying Systems. 14th Week Tracking of Time-Varying Systems.
RECOMENDED OR REQUIRED READING	1. M.H. Hayes, Statistical Digital Signal Processing, and Modeling, John Wiley, 1996. 2. Haykin, Adaptive Filter Theory, 4th edition , Prentice Hall, 2002.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 35 Project 1 25 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 3 3 Preparation for Quiz 0 0 0 Individual or group work 14 8 112 Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 30 30 Laboratory (including preparation) 0 0 0 Final exam 1 3 3 Homework 2 35 70 Total Workload 300 Total Workload / 30 10 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No