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CSCI-C 463 Artificial Intelligence I

3 credits

  • Prerequisites: None
  • Delivery: On-Campus
  • Goals of artificial intelligence, relations with other fields. Introduction to knowledge representation and inference: predicate calculus, frames, semantic networks, and connectionist representation schemes. Pattern recognition and pattern association. Computer vision. Natural language processing: speech recognition, syntax, and semantics. Heuristic search. Extensive laboratory exercises.

    Learning Outcomes

    1. Explain the history of artificial intelligence (AI) and its cultural and philosophical background.
    2. Apply AI approaches to achieve AI goals.
    3. Explain search and representation strategies in AI.
    4. Evaluate the strengths and weaknesses of algorithms used in AI subfields, such as machine learning, natural language processing, planning, robotics, and autonomous agents.
    5. Propose AI solutions to real-world problems.
    6. Write critically about AI research and development topics in the literature.



    There is not a syllabus available for this course.