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Artificial Intelligent

Course Code C0173 Th 4 Pr 4 CrHrs 6

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving", however this definition is rejected by major AI researchers.

  The course is to study recent developments in intelligent systems, allowing the students to

acquire the ability to understand the intelligence and solving problems and search in many   techniques in order to find the goals

Distribution of Marks

Final Mark

Final Exam

Second Term

Mid-Year

First Term

100

Prac.

Theor.

Prac.

Theor.

Prac.

Theor.

Prac.

Theor.

20

30

5

5

10

20

5

5

References

SN

Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.

1

Kochenderfer, Mykel J. Decision making under uncertainty: theory and application. MIT press, 2015.

2

Plomin, Robert, and Sophie von Stumm. "The new genetics of intelligence." Nature Reviews Genetics 19.3 (2018): 148.

3

Do, H., F. Massa, and T. Tison. "Using fuzzy logic control approach and model reduction for solving frictional contact problems." Engineering Computations 33.4 (2016): 1006-1032..

4

Subject

Week

  
 

Introduction to Intelligent System

First

 

Approaches Based Goal of Computational Model

Second

 

Techniques that make the system to behave as Intelligent

Third

 

Problem Solving, Search and Control Strategies

Fourth

 

Problem Solving, States

Fifth

 

Problem Solution and description

Sixth

 

Computational Complexity

Seventh

 

Tree Structure

Eighth

 

Search Algorithms

Ninth

 

Generate and test search

Tenth

  

Best first search

Eleventh

 

Greedy Search

Twelfth

 

A* Search, Constraint Search

Thirteenth

 

Control Strategies

Fourteenth

 

Decision Making

Fifteenth

 

Mid-term exams

Sixteenth

 

Introduction to Machine Learning

Seventeenth

 

Algorithms

Eighteenth

 

Linear Algebra

Nineteenth

 

Probability

Twentieth

 

Learning and Expert Systems

Twenty-First

 

Natural Language Processing

Twenty-Second

 

Deep Learning

Twenty-Third

 

Introduction to Neural Networks

Twenty-Fourth

 

Neural Network Architectures

Twenty-Fifth

 

Single Layer Neural Network System

Twenty-Sixth

 

Applications of Neural Networks

Twenty-Seventh

 

Multi-Layer Neural Network System

Twenty-Eighth

 

Back Propagation Neural Network 1

Twenty-Ninth

 

Back Propagation Neural Network 2

Thirtieth

 

Genetics

Thirty-First

 

Fuzzy Logic

Thirty-Second

 

Robotics

Thirty-Third