Introduction To Artificial Intelligence

Catalog description

This course covers the theory and methodologies associated with the development of Artificial Intelligence based systems. Topics covered include: Intelligent agents, problem solving via search methods, knowledge representation and reasoning, uncertain knowledge and probabilistic reasoning, learning and reinforcement learning, communicating, perceiving, and acting, natural language processing and robotics, and other special topics. Typically offered Fall Spring Summer. Prerequisite(s): ITS 14000 FOR LEVEL UG WITH MIN. GRADE OF C OR CS 12300 FOR LEVEL UG WITH MIN. GRADE OF C OR CIS 16600 FOR LEVEL UG WITH MIN. GRADE OF C Course Learning Outcomes 1. Understand the role and importance of artificial intelligence principles. 2. Understand the problem solving in Artificial Intelligence systems. 3. Understand and explain decision making in Artificial Intelligence systems. 4. Understand and explain learning in Artificial Intelligence systems. 5. Understand and demonstrate Reinforcement learning basic and concepts. 6. Understand and demonstrate Machine learning basic and concepts. 7. Understand and demonstrate Deep learning basic and concepts. open-source tools. 8. Understand and implement data cleaning process. 9. Demonstrate the implementation of the model utilizing open-source numerical modules and packages. View Class Schedule