Advance Computer Simulation and Modeling

This course will cover the advance topics of the techniques of modeling and computer simulation and modeling. After familiarizing ourselves with the fundamentals of Fuzzy Modeling and Inference, Bayesian Networks, Data Clustering and Markov Chains, we will dig into applications that illustrate the use of these techniques. The applications covered will be from computer systems, computer networking and some topic in machine learning. We will also study computational techniques to complement analytical performance evaluation.

Course References
1 Computer Modeling and Simulation (In Persian)M. Vafaei Jahan, Islamic Azad University – Mashhad Branch Press, 2011.
2 Finite Markov Chain and Algorithmic Application, O. Haggstrom, Cambridge University Press, 2002.
3 Markov Processes for Stochastic Modeling, Oliver C. Ibe, Elsevier Press, 2009.
4 Markov Process and Applications, Algorithms, Networks, Genome and Finance, F.Pardoux, Wiley 2008.
5 Test Functions for Optimization Needs, M.Molga, C.Smutnicki, 2005.
Useful Tools
1 RapidMiner: RapidMiner is unquestionably the world-leading open-source system for data mining
Course Topics
Fuzzy Modeling and Inference (6 hours)
Bayesian Networks (6 hours) – Video
Web Robot Detection Benchmark Data (Create By S.Layeghi & A.H Zarei)
Markov Process and Markov Chains ( 6 hours)
Markov Chain Example I
Random Walks in Graph – (Slide ppt) (2 hours)
Data Clustering (6 hours)
Markov Clustering vs. k-means and DBSCAN
#1 Select scientific paper for presentation (3 hours)
Hidden Markov Model (HMM) – (6 hours)
Markov Decision Process (MDP) – (6 hours)
– MDP Example
#2 Paper Presentation
Instance of Exam and Practice
Reading List Papers
Course Evaluation
Each Exercise = +0.5
* Paper Presentation = 2 / 20
* Final Exam = 14 / 20
* Take Home and Represent as Paper Format = 3 / 20
Conference Paper = 2 / 20 (+1)