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 Selda Güney
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the statistical decision theory concepts and criteria for the decision. 2) Know the basic structure of the transformations and applies them. 3) Learn the detection of signal with noises. 4) Know the basic models for signal detection. 5) Know and applies the methods used for parameter estimation. 6) Learn the Wiener and Kalman filters.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Detection, estimation and filter theory with applications in Communications and Signal Processing. MAP and MSE detection theories, Wiener filtering.
COURSE CONTENTS	WEEK TOPICS 1st Week Detection, estimation and filter theory 2nd Week Detection, estimation and filter theory 3rd Week Detection, estimation and filter theory 4th Week Applications in Communications and Signal Processing 5th Week Applications in Communications and Signal Processing 6th Week Applications in Communications and Signal Processing 7th Week Probability theory 8th Week Midterm Exam 9th Week Probability theory 10th Week MAP and MSE detection theories 11th Week MAP and MSE detection theories 12th Week MAP and MSE detection theories 13th Week Wiener filtering 14th Week Wiener filtering
RECOMENDED OR REQUIRED READING	1. M.D. Srinath, P.K. Rajasekaran, R. Viswanathan, Introduction to statistical signal processing with applications Englewood Cliffs, N.J. Prentice Hall, c1996 2. H. Vincent Poor, An introduction to signal detection and estimation, 2nd Ed., New York. Springer-Verlag, c1994 3. Harry L. Van Trees, Detection, estimation, and modulation theory part 1, New York, Wiley 1968, 2001
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 2 20 Total(%) 50 Contribution of In-term Studies to Overall Grade(%) 50 Contribution of Final Examination to Overall Grade(%) 50 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