Computing & Information Technology  Cairo

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Introduction to Artificial Intelligence

  • Computing & Information Technology |

Description

Introduction to basic methods of Artificial Intelligence (AI) such as problem solving, searching techniques, machine learning and knowledge representation. Through discussions, small projects, and examples, students learn what AI is, some of the major developments in the field, promising directions, and the techniques for making computers exhibit intelligent behavior. Students make use of available tools and explore some areas of applications.

Program

Multimedia and Graphics Program.

Objectives

  • 1. Understand the basic concepts of artificial intelligence.
    2. Understand state space representation.
    3. Compare different problem solving strategies based on algorithms and heuristics.
    4. Understand the basic concepts of Genetic Algorithm.
    5. Understand the basic concepts of machine learning using artificial neural networks.
    6. Understand different Methods for knowledge representations.

Textbook

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson.

Course Content

content serial Description
1Introduction to AI: Definition, History and Goals.
2State Space Representation.
3Blind search techniques
4Heuristic search techniques.
5A* Algorithm.
6Admissibility, Monotonicity and Informedness of a heuristic function.
77th Week Examination
8Game trees and Alpha Beta Pruning Algorithm.
9Genetic Algorithm.
10Introduction to Machine Learning using Artificial Neural Networks
11Perceptron Learning Algorithm.
1212th Week Examination
13Knowledge-based systems.
14Propositional Logic.
15Revision
16Final Examination
1Introduction to AI: Definition, History and Goals.
2State Space Representation.
3Blind search techniques
4Heuristic search techniques.
5A* Algorithm.
6Admissibility, Monotonicity and Informedness of a heuristic function.
77th Week Examination
8Game trees and Alpha Beta Pruning Algorithm.
9Genetic Algorithm.
10Introduction to Machine Learning using Artificial Neural Networks
11Perceptron Learning Algorithm.
1212th Week Examination
13Knowledge-based systems.
14Propositional Logic.
15Revision
16Final Examination
1Introduction to AI: Definition, History and Goals.
2State Space Representation.
3Blind search techniques
4Heuristic search techniques.
5A* Algorithm.
6Admissibility, Monotonicity and Informedness of a heuristic function.
77th Week Examination
8Game trees and Alpha Beta Pruning Algorithm.
9Genetic Algorithm.
10Introduction to Machine Learning using Artificial Neural Networks
11Perceptron Learning Algorithm.
1212th Week Examination
13Knowledge-based systems.
14Propositional Logic.
15Revision
16Final Examination
1Introduction to AI: Definition, History and Goals.
2State Space Representation.
3Blind search techniques
4Heuristic search techniques.
5A* Algorithm.
6Admissibility, Monotonicity and Informedness of a heuristic function.
77th Week Examination
8Game trees and Alpha Beta Pruning Algorithm.
9Genetic Algorithm.
10Introduction to Machine Learning using Artificial Neural Networks
11Perceptron Learning Algorithm.
1212th Week Examination
13Knowledge-based systems.
14Propositional Logic.
15Revision
16Final Examination

Markets and Career

  • Generation, transmission, distribution and utilization of electrical power for public and private sectors to secure both continuous and emergency demands.
  • Electrical power feeding for civil and military marine and aviation utilities.
  • Electrical works in construction engineering.
Courses
  • Degree Bachelor
  • Code: CS366
  • Credit hrs: 3
  • Prequisites: CS212, CS202
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