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The candidate can explain the value of intelligent systems for sustainable processes, services, or systems. The candidate can present problems and relevant solution methods in a professional and scientific manner. The candidate can discuss ethical challenges of artificial intelligence.
demonstrate good knowledge of basic theoretical foundations of the following common intelligent systems. methodologies: Rule-based systems. Fuzzy inferencing. Artificial neural networks. Evolutionary computation. Data Mining. Case-based reasoning. Probabilistic reasoning. Intelligent agents .
An introduction to the theories and algorithms used to create intelligent systems. Topics include search algorithms (e.g. A*, iterative deepening), logic, planning, knowledge representation, machine learning, and applications from areas such as computer vision, robotics, natural language processing, and expert systems.
Assess the validity of approaches to model intelligent processing; Select appropriately from a range of techniques for intelligent systems; Assess the applicability of AI techniques in novel domains; Assess the claims of AI practitioners as they relate to `intelligence' Knowledge and Understanding
This is a learning material for Intelligent Systems republic of the philippines biliran province state university (formerly known as naval state university) iso.
This is an introductory course into the field of artificial intelligence (AI), with particular focus on search as the fundamental technique for solving AI problems. The problem of navigating a road map with a known layout is a typical example of a problem studied in this course.
Course Number. HI 192. Course Title. Knowledge Representation and Health Decision Support. urse PrerequisiteSenior StandingCourse DescriptionBiomedical decision making and it. applications to computer based decision support tools. Bayesian statistics, belief networks and influence diagrams; Computational approa. Lecture Outline.
