EXPERT SYSTEMS DEVELOPMENT

Course Description

This course is intended to give an overview about artificial intelligence and explain the state of the art of the expert system technologies. Based on the main concepts, course will include the components of expert systems, knowledge acquisition and validation. In addition, different techniques of knowledge representation and the related programming languages as well as the related tools will be covered. Besides, inference processes, explanation and reasoning under uncertainty the course will examine the expert system architecture, classification of expert systems, and the phases of building an expert system. Moreover, searching techniques used in AI, A programming language in Prolog AI in the areas of application of expert systems will be covered in this course.

EXPERT SYSTEMS DEVELOPMENT

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Course outcomes

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Course Syllabus

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Learning Outcomes

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Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

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