ARTIFICIAL INTELLIGENCE PARADIGMS

Credit Hours: 3 · Graduate

This course provides a broad survey of foundational and contemporary paradigms in Artificial Intelligence (AI), with a focus on approaches beyond traditional Machine Learning (ML). Students will explore a range of AI methods including symbolic reasoning, search and planning, evolutionary computation, multi-agent systems, information retrieval, transfer learning, generative models, and explainability. Emphasis is placed on understanding the theoretical foundations, practical applications, and limitations of each approach. Through hands-on labs, in-class activities, and assignments, students will gain experience implementing and analyzing AI systems in practical settings, and evaluating their suitability for different problem domains. This course is suitable for students seeking a comprehensive overview of the AI field, including those transitioning into or advancing within computer science and data science careers. In order to enroll in an online section of this course, you are expected to have a working camera and microphone. During class sessions, you must be able to participate using your microphone and you are expected to have your camera on for the duration of the class so that you are visible to the instructor and other students in the class, just as you would be in an in-person class. Recommended Prerequisite(s): COMP 647 or COMP 642 .