Course Description
Course Aims and objectives
Course outlines
Text book and references
Prerequisite
Grading policy
Instructors and office hours
Syllabus
Time table
Lecture Notes
Attached Files
Previous Exams
Course Description
| Artificial Intelligent (AI) focus on developing machine that can think. AI can be defined as a branch of computer science that is concerned in the automation of intelligent behavior. By Intelligent we mean that do the right thing given a set of inputs and a variety of actions. The AI fields begin formally in 1956. The hope at that time is to build a machine that can think like human in all aspects. However, until now that hope is not successful due to the lack of understanding of the human brain. In another point of view, we do have many successful AI applications that make computers even more intelligent than us. One example is the Deep Blue system which defeated the world chess champion. The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, and machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem solving and for reasoning. In fact, the skill for problem solving itself should be acquired through learning. Also, methods for problem solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition. In this course, we will study the most fundamental knowledge for understanding AI. We will introduce some basic search algorithms for problem solving; knowledge representation and reasoning; Genetic Programming; and neural networks as well as machine learning. |
Course Aims And Objective
| At the end of this course, students should have: ? Further developed efficient strategies for learning. ? Gained an appreciation of Artificial Intelligence (AI) as a sub-area of computer science. ? Thoroughly understood, and been able to implement, the various methods of accomplishing a state space search. ? Understood the importance of knowledge to the problem solving process and the difficulties associated with the acquisition of this knowledge. ? Appreciated the need to have various knowledge representation methods to suit particular types of problems. ? Been able to identify the knowledge representation method for a particular problem definition. ? A relatively in depth knowledge of two other sub-fields of AI (excluding search and knowledge representation). ? An appreciation of the need for AI in real world situation. ; |
Course Outline
| Introduction | |
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| Intelligent agent | |
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| Problem Formulation and Search | |
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| Beyond Classical Search | |
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| Adversarial Search | |
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| Constraint Satisfaction Problems | |
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| Knowledge Representation Logical Agents | |
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| Knowledge Representation First-Order Logic | |
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| Inference in First Order Logic | |
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| Classical Planning | |
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| Planning and Acting in the Real World | |
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| Quantifying Uncertainty | |
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| Probabilistic Reasoning | |
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| Learning | |
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Text book and references
| The Main Book | |
| Title | Artificial Intelligence, A modern Approach:- Stuart Russell, Peter Norvig. |
| Author(s) | |
| Edition | Second Edition |
| Publisher | Prentice Hall |
| The References Book | |
| Title | Artificial Intelligence,Structures and Strategies for Complex problem Solving |
| Author(s) | 3. George F. Luger & William A. Stubblefield |
| Edition | Fifth edition, |
| Publisher | Addison Wesley, 2005. |
| Title | |
| Author(s) | 2. Ivan Bratko |
| Edition | Third edition, |
| Publisher | Addison Wesley |
Grading Policy
| Activity | Weight |
Instructors And Office Hours
| The Instructors of the Course | |
| Name: | مامون سليمان عبدالرحمن الربابعه |
| Office Hours: | |
| Office Phone: | |
| Instrcutor Mail: | marabahaa |
Miscellaneous
Time Table
| Subject | Hours |
| Attachment Files |
Previous Exams
| Previous Exams |