PSS702 - STOCHASTIC MODELLING

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
STOCHASTIC MODELLING PSS702 2nd Semester 3 0 3 10
Prequisites
Course languageEnglish
Course typeMust 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
 
Instructor (s)Prof. Dr. ArmaÄŸan Tarım 
Course objectiveThis course aims to provide the student with the skills to understand and implement the advance mathematical modeling techniques. 
Learning outcomes
  1. - is able to identify the most suitable method to model decision problems under uncertainty
  2. - is able to model decision problems under uncertainty by means of various tecniques
  3. - is able to solve decision problems by using suitable computer softwares
Course ContentStochastic programming, Stochastic Dynamic Programming, Markov Decision Processes, Simulation 
References- A First Course in Probability, S. Ross. Pearson Education, 2002.  

Course outline weekly

WeeksTopics
Week 1Introduction: Decision Making Under Uncertainty
Week 2Stochastic Programming
Week 3Stochastic Programming
Week 4Dynamic Programming
Week 5Stochastic Dynamic Programming
Week 6Stochastic Dynamic Programming
Week 7Mid-term Exam
Week 8Approximate Dynamic Programming
Week 9Markov Models
Week 10Markov Decision Processes
Week 11Queuing Models
Week 12Queuing Models and Simulation
Week 13Project Presentations I
Week 14Project Presentations II
Week 15Project Presentations III
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance145
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation110
Project120
Seminar00
Midterms115
Final exam150
Total100
Percentage of semester activities contributing grade succes1750
Percentage of final exam contributing grade succes150
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation199
Project12525
Homework assignment000
Midterms (Study duration)13535
Final Exam (Study duration) 15555
Total Workload32133250

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest