Tuesday, March 25, 2008

Course description 4

11. Probability & Statistics
Short description, sample space and axiomatic definition of a probability measure. Probabilistic models of the real line. Conditional probability and independence. Random variables and probability distributions. Main discrete and continuous random variables. Expectation, moments and characteristic functions. Independence of random variables. Multidimensional gaussian laws. Different kinds of convergence of sequences of random variables. Law of Large Numbers and Central Limit Theorem.
12. Mathematical Modeling
Short description, Modeling process , Modeling Change , the elementary grade model, stability model. simulation modeling, discrete probabilistic modeling, graphs of functions as modeling and examples.

13. Matlab 1
Short description, introduction to the fundamentals of MATLAB functions and programming, the class proceeds to address the mainstream areas of image processing. Compiler, a new function was written and documented, M-files, code optimization. Cell arrays and structure, visualizing data and function, notebook, M-functions, generating and plotting image histograms etc..
14. Database Management System
Short description, brief introduction to data models and formal languages for databases , how to develop server-side and client-side database applications and analyze business intelligence data. All of the brand-new features, Reporting Services, Integration Services, Notification Services, and Service Broker, create custom management scripts with SQLCMD, and improve performance with SQL Profiler, relation data theories, database design. Using SQL both as a query and data manipulation language, designing and querying databases, Entity-Relationship.

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