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  Courses 

 Advanced Topics (Big Data Analytics)(SPRING_2017)

Aims:

Introducing methods of analyzing big data

Syllabus:

  • An introduction to Analyzing Big Data
  • MapReduce programming
  • Finding Similar Items
  • Mining Data Streams
  • Frequent Itemsets

Text Book:




 ٍEngineering Statistics(SPRING_2017)

Aims:

This course provides an introduction to Probability and statistics with applications.

Syllabus:

  • axioms of probability
  • basic probability concepts and models (counting methods , conditional probability, Bayes theorem,et.)
  • Random Variables and Probability Distributions
  • Random Variables and Probability Distributions (continue)
  • Mathematical Expectation
  • Some Discrete Probability Distributions
  • Some Continues random variables
  • Moment-generating function
  • Normal Distribution
  • Functions of random variables
  • Introduction to statistics
  • Fundamental Sampling Distributions and Data Descriptions
  • One- and Two-Sample Estimation Problems
  • Tests of Hypotheses
  • Simple Linear Regression and Correlation
  • Nonlinear Regression Models

Text Book:

  • Probability and Statistics for Engineers and Scientists (9th Edition)و Ronald E. Walpole,Roanoke College, Raymond H. Myersو Prentice Hall
  • Probability, Statistics, and Random Processes for Electrical Engineering Third Edition, Alberto Leon-Garcia, University of Toronto


 ٍEngineering Statistics(SPRING_2017)

Aims:

This course provides an introduction to Probability and statistics with applications.

Syllabus:

  • axioms of probability
  • basic probability concepts and models (counting methods , conditional probability, Bayes theorem,et.)
  • Random Variables and Probability Distributions
  • Random Variables and Probability Distributions (continue)
  • Mathematical Expectation
  • Some Discrete Probability Distributions
  • Some Continues random variables
  • Moment-generating function
  • Normal Distribution
  • Functions of random variables
  • Introduction to statistics
  • Fundamental Sampling Distributions and Data Descriptions
  • One- and Two-Sample Estimation Problems
  • Tests of Hypotheses
  • Simple Linear Regression and Correlation
  • Nonlinear Regression Models

Text Book:

  • Probability and Statistics for Engineers and Scientists (9th Edition)و Ronald E. Walpole,Roanoke College, Raymond H. Myersو Prentice Hall
  • Probability, Statistics, and Random Processes for Electrical Engineering Third Edition, Alberto Leon-Garcia, University of Toronto


 Special Topics (Algorithms for Complex Networks)(SPRING_2017)

Aims:

An Intoduction to the Algorithms of Analysing Complex Networks

Syllabus:

  • Introduction to Complex Networks
  • measures and metric of Graphs
  • Random Graphs
  • Power low and evolution of networks
  • Spectral Analysis of Networks
  • Clustering methods for big graphs
  • community detection
  • Graph similarity and alignment
  • cascading an information flow
  • Epidermic
  • Influence Analysis
  • Link prediction in Complex Networks
  • Game Theory in Social Networks
  • multilayer complex networks
  • Compression and Visualization of Complex networks

Text Book:

  • M. Newman, A.-L. Barabasi, and D. J. Watts, The structure and dynamics of networks: Princeton University Press, 2006.
  • S. Wasserman and K. Faust, Social network analysis: Methods and applications: Cambridge university press, 1994.
  • A. Rajaraman and J. D. Ullman, Mining of massive datasets: Cambridge University Press, 2011.


 Advanced Topics (Big Data Analytics)(FALL_2016)

Aims:

Introducing methods of analyzing big data

Syllabus:

  • An introduction to Analyzing Big Data
  • MapReduce programming
  • Finding Similar Items
  • Mining Data Streams
  • Link Analysis
  • Frequent Itemsets
  • Clustering
  • Recommendation Systems
  • Mining Social-Network Graphs
  • Dimensionality Reduction
  • Large-Scale Machine Learning

Text Book:

  • Mining of Massive Datasets , by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Stanford university


 Fuzzy Systems & Method(FALL_2016)

Aims:

introducing the concepts, methods and the fuzzy systems

Syllabus:

  • Fuzzy sets
  • Fuzzy relations
  • extension principle
  • Fuzzy logic
  • Fuzzy neural networks
  • Fuzzy Control
  • Fuzzy applications

Text Book:

  • Fuzzy set and Fuzzy logic (Theory and application), G.J Klir, B. Yoan
  • Fuzzy set and application , R.R. Yager, H.T. Nguyen.
  • Introduction To Fuzzy Arithmetic, A. Kaufman.


 Principles of Database Design(FALL_2016)

Aims:

PRINCIPLES OF DATABASE DESIGN

Syllabus:

  • Introduction to DBMS
  • Introduction to the Relational Model
  • Introduction to the Relational Model-2
  • Relational Algebra
  • Relational Algebra-2
  • Introduction to SQL-1
  • Introduction to SQL-2
  • Database Design: The Entity-Relationship Approach-1
  • Database Design: The Entity-Relationship Approach-2
  • Relational Database Design
  • Normalization-1
  • Normalization-2

Text Book:

  • Database System Concepts 6th edition, Avi Silberschatz, Henry F. Korth, S. Sudarshan


 ٍEngineering Statistics(SPRING_2016)

Aims:

This course provides an introduction to Probability and statistics with applications.

Syllabus:

  • axioms of probability
  • basic probability concepts and models (counting methods , conditional probability, Bayes theorem,et.)
  • Random Variables and Probability Distributions
  • Random Variables and Probability Distributions (continue)
  • Mathematical Expectation
  • Some Discrete Probability Distributions
  • Some Continues random variables
  • Moment-generating function
  • Normal Distribution
  • Functions of random variables
  • Introduction to statistics
  • Fundamental Sampling Distributions and Data Descriptions
  • One- and Two-Sample Estimation Problems
  • Tests of Hypotheses
  • Simple Linear Regression and Correlation
  • Nonlinear Regression Models

Text Book:

  • Probability and Statistics for Engineers and Scientists (9th Edition)و Ronald E. Walpole,Roanoke College, Raymond H. Myersو Prentice Hall
  • Probability, Statistics, and Random Processes for Electrical Engineering Third Edition, Alberto Leon-Garcia, University of Toronto


 ٍEngineering Statistics(SPRING_2016)

Aims:

This course provides an introduction to Probability and statistics with applications.

Syllabus:

  • axioms of probability
  • basic probability concepts and models (counting methods , conditional probability, Bayes theorem,et.)
  • Random Variables and Probability Distributions
  • Random Variables and Probability Distributions (continue)
  • Mathematical Expectation
  • Some Discrete Probability Distributions
  • Some Continues random variables
  • Moment-generating function
  • Normal Distribution
  • Functions of random variables
  • Introduction to statistics
  • Fundamental Sampling Distributions and Data Descriptions
  • One- and Two-Sample Estimation Problems
  • Tests of Hypotheses
  • Simple Linear Regression and Correlation
  • Nonlinear Regression Models

Text Book:

  • Probability and Statistics for Engineers and Scientists (9th Edition)و Ronald E. Walpole,Roanoke College, Raymond H. Myersو Prentice Hall
  • Probability, Statistics, and Random Processes for Electrical Engineering Third Edition, Alberto Leon-Garcia, University of Toronto


 Special Topics (Algorithms for Complex Networks)(SPRING_2016)

Aims:

An Intoduction to the Algorithms of Analysing Complex Networks

Syllabus:

  • Introduction to Complex Networks
  • measures and metric of Graphs
  • Random Graphs
  • Power low and evolution of networks
  • Spectral Analysis of Networks
  • Clustering methods for big graphs
  • community detection
  • Topic modeling
  • Graph similarity and alignment
  • cascading an information flow
  • Epidermic
  • Influence Analysis
  • Anomaly detection
  • Game Theory in Social Networks
  • Complex network analysis applications
  • Compression and Visualization of Complex networks

Text Book:

  • M. Newman, A.-L. Barabasi, and D. J. Watts, The structure and dynamics of networks: Princeton University Press, 2006.
  • S. Wasserman and K. Faust, Social network analysis: Methods and applications: Cambridge university press, 1994.
  • A. Rajaraman and J. D. Ullman, Mining of massive datasets: Cambridge University Press, 2011.


 Advanced Topics (Big Data Analytics)(FALL_2015)

Aims:

Introducing methods of analyzing big data

Syllabus:

  • An introduction to Analyzing Big Data
  • MapReduce programming
  • Finding Similar Items
  • Mining Data Streams
  • Link Analysis
  • Frequent Itemsets
  • Clustering
  • Recommendation Systems
  • Mining Social-Network Graphs
  • Dimensionality Reduction
  • Large-Scale Machine Learning

Text Book:

  • Mining of Massive Datasets , by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Stanford university


 Fuzzy Systems & Method(FALL_2015)

Aims:

introducing the concepts, methods and the fuzzy systems

Syllabus:

  • Fuzzy sets
  • Fuzzy relations
  • extension principle
  • Fuzzy logic
  • Fuzzy neural networks
  • Fuzzy Control
  • Fuzzy applications

Text Book:

  • Fuzzy set and Fuzzy logic (Theory and application), G.J Klir, B. Yoan
  • Fuzzy set and application , R.R. Yager, H.T. Nguyen.
  • Introduction To Fuzzy Arithmetic, A. Kaufman.


 Principles of Database Design(FALL_2015)

Aims:

PRINCIPLES OF DATABASE DESIGN

Syllabus:

  • Introduction to DBMS
  • Introduction to the Relational Model
  • Introduction to SQL
  • Relational Algebra
  • Database Design: The Entity-Relationship Approach
  • Relational Database Design
  • Object-Based Databases

Text Book:

  • Database System Concepts 6th edition, Avi Silberschatz, Henry F. Korth, S. Sudarshan


 
 
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