COURSE UNIT TITLE

: RANDOM VARIABLES AND STOCHASTIC PROCESSES

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5129 RANDOM VARIABLES AND STOCHASTIC PROCESSES ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR DAMLA GÜRKAN KUNTALP

Offered to

Ph.D. in Computer Science (English)
Computer Science
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

The goal of this course is to introduce the students into techniques of statistical signal analysis through the concepts of random variables and stochastic processes, and to equip the students with the ability of simulating applications of stochastic processes using MATLAB programming language.

Learning Outcomes of the Course Unit

1   To be able to define concepts of random variables and stochastic processes.
2   To be able to differentiate deterministic and random signals and their properties.
3   To be able to formulate problems of probabilistic nature using the tools and terminolgy of statistical signal analysis.
4   To be able to analyze random signals using MATLAB programming language.
5   To be able to propose different solutions into problems of statistical signal analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Random Variables
2 Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Important Random Variables
3 Expectation and Moments
4 Multiple Random Variables
5 Sums of Random Variables
6 Central Limit Theorem
7 Stochastic Processes
8 Systems with Random Inputs
9 Frequency Analysis of Stochastic Processes (Power Spectrum Density)
10 Gaussian Stochastic Processes
11 Poisson Stochastic Processes
12 Markov Chains
13 Markov Processes
14 Discussion

Recomended or Required Reading

Textbook: Intiutive Probability and Random Processes using MATLAB, S. Kay, Springer, 2006.
Supplemantary book: Probability, Statistics, and Random Processes for Electrical Engineering, (3rd edt.), A. Leon-Garcia, Prentice Hall, 2008.
Other course materials: Course notes.

Planned Learning Activities and Teaching Methods

Lecture+Quizzes+Homeworks+Data homework+Exam

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +PRS * 0.20 + FIN * 0.30
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 +ASG * 0.20 +PRS * 0.20 +MARKRST * 0.30


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

olcay.akay@deu.edu.tr
damla.kuntalp@deu.edu.tr

Office Hours

To be announced during the semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Design Project 1 60 60
Preparing presentations 1 25 25
Preparation for midterm exam 1 15 15
Preparation for final exam 1 25 25
Preparations before/after weekly lectures 12 4 48
Final 1 3 3
Midterm 1 2 2
TOTAL WORKLOAD (hours) 220

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.14121
LO.23212412
LO.3211
LO.421125
LO.51253111111

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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