AI Artificial Intelligence

COURSE TITLE:          Artificial Intelligence 

Credit Hours:            3 (Theory)

Pre-requisites:          Data Structures and Algorithms

 

COURSE OBJECTIVES:

 

To introduce the foundations of artificial intelligence.

 

ESSENTIAL TOPICS TO BE COVERED:

·         Expert systems

·         Natural language processing

·         Knowledge engineering

 

COURSE DESCRIPTION:

Introduction to Artificial Intelligence, Basic elements of AI, history, applications and

classification of techniques used. Production Systems and Search: Definition and examples of Production Systems. State Space Search: graph theory, strategies (data driven, goal driven), techniques (depth first, breadth first, etc.). Heuristic Search: definitions, techniques: hill climbing etc. Knowledge Representation: Knowledge representation issues, Procedural Knowledge Representation vs. Declarative Knowledge, Reasoning. Facts, Representing Knowledge using Rules, Logic Programming. Common Sense and Statistical Reasoning: Nonmonotonic reasoning

and modal logic for nonmonotonic reasoning. How to deal with Agents and their Beliefs. Use of Certainty Factors in Rule-Based Systems. Associating probabilities to assertions in first-order logic. Bayesian Networks. Expert Systems: Components of expert systems, development methodology (selection of problems, knowledge engineering), types (rule based, model based, case based), knowledge representation (rules, semantic networks, frames), inference, forward chaining, backward chaining, production systems and rule based expert systems. goal driven problem reasoning, data driven reasoning. (same as TE outline)

 

Recommended Text(s):

  • Artificial Intelligence: A Modern Approach, 2nd Ed., Stuart J. Russell and Peter Norvig, Prentice Hall, 2002.
  • Artificial Intelligence, 2nd Ed., Elaine Rich and Kevin Knight, McGraw-Hill 1990.
  • Artificial Intelligence in Engineering Approach, R. J. Schalkoff, McGraw Hill, 1990.
  • Introduction to Expert Systems, 3rd Ed, Peter Jackson, Addison Wesley, 1998.
  • Prolog Programming for Artificial Intelligence, 3rd Ed., Ivan Bratko, Addison Wesley 2000.
  • Under Standing Artificial Intelligence by Henry C. Mishkof. 
  • Artificial Intelligence by Luger.

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NC Numerical Computing

Course Name:            Numerical Computing

 

Credit Hours:              3 (2+1)

Prerequisites:            Calculus and Analytical Geometry

 

 

Course Outline:

 

 

The concepts of efficiency, reliability and accuracy of a method; Minimizing computational errors; Theory of Differences, Difference Operators, Difference Tables, Forward Differences, Backward Differences and Central Differences. Mathematical Preliminaries, Solution of Equations in one variable, Interpolation and Polynomial Approximation, Numerical Differentiation and Numerical Integration, Initial Value Problems for Ordinary Differential Equations, Direct Methods for Solving Linear Systems, Iterative Techniques in Matrix Algebra, Solution of non-linear equations.

 

 

Reference Materials:

 

 

1.                  Numerical Methods in Scientific Computing by Germund, D. Åke, B.

 

2.                  Numerical Methods for Scientific Computing by J. H. Heinbockel.

 

3.                  Numerical Analysis by I. A. Khubaza.

 

4.                  Numerical Analysis and Programming by Shan S Kuo.

5.                  Numerical Analysis by Berden, F.

 

6.         Numerical Analysis by Gerald.


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C&C Compiler Construction

Course Name:            Compiler Construction

 

Credit Hours:               3 (2+1)

Prerequisites:              Theory of Automata

 

 

Course Outline:

 

 

Introduction to interpreter and compiler. Compiler techniques and methodology; Organization of compilers; Lexical and syntax analysis; Parsing techniques. Types of parsers, top-down parsing, bottom-up parsing, Type checking, Semantic analyser, Object code generation and optimization, detection and recovery from errors.

 

 

Reference Materials:

 

 

1. Compilers: Principles, Techniques, and Tools By Alfred V. Aho, Ravi Sethi, Jeffrey D. Ullman,  Contributor Jeffrey D. Ullman, Addison-Wesley Pub. Co., 2nd edition, 2006 Original from the University of Michigan

 

2. Modern Compiler Design, by Dick Grune, Henri E. Bal, Ceriel J. H. Jacobs, Koen G.         Langendoen, John Wiley, 2000.

 

3. Modern Compiler Implementation in C, by Andrew W. Appel, Maia Ginsburg, Contributor        Maia Ginsburg, Cambridge University Press, 2004.

 

4. Modern Compiler Design by Dick Grune, Henri E. Bal, Ceriel J. H. Jacobs, Koen G.Langendoen, 2003, John Wiley & Sons


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D&W Data Warehousing

Course Name:           Data Warehousing

 

Credit Hours:               3 (2+1)

Prerequisites:             Database Systems

 

Course Outline:

 

 

Introduction of the business context for data warehousing and decision support systems. Differences between TPS and DSS environments. Data extraction, transformation and loading (ETL and ELT), Data warehouse Architecture. Data Marts. Differentiate Data Marts and Data Warehouse. Data Warehouse Design Methodology: De -normalization and Dimensional Modelling. Online analytical processing (OLAP) and data aggregations. Indexing techniques used in data warehousing. Hardware and software systems consideration for data warehousing. Data warehouse maintenance.

 

Reference Materials:

 

 

1.                  Data Warehousing Fundamentals, 2nd Edition, Paulraj Ponniah, 2010, John Wiley             & Sons Inc., NY.

 

2.                  Building the Data Warehouse, 4th Edition, W. H. Inmon, 2005, John Wiley & Sons Inc.,     NY.

 

3.                  The Data Warehouse Toolkit, 2nd Edition Ralph Kimball and Margy Ross, 2002, John             Wiley   & Sons Inc., NY.


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