Aug 10, 2022  
2019-2020 Graduate Academic Catalog 
    
2019-2020 Graduate Academic Catalog [ARCHIVED CATALOG]

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CS 5881 - Artificial Intelligence

3 lecture hours 0 lab hours 3 credits
Course Description
This course provides an introduction to basic concepts of artificially intelligent systems. Topics covered include knowledge representation, search strategies and machine learning. The course introduces modern machine learning techniques for supervised, unsupervised, and reinforcement learning and describes the role of artificial intelligence (AI) in engineering and computing systems. Practical exercises permit students to apply AI tools and languages to suitable problems. (prereq: CS 2852, MA 2310, or equivalent or consent of instructor)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Demonstrate an understanding of the principles of formal logic including propositional and first order logic
  • Conduct proofs of correctness in reasoning systems using the methods of unification and resolution
  • Understand the techniques involved with reasoning in the presence of uncertainty
  • Address the problems related to search, and its application to intelligent systems, including game playing, decision making, and adversarial search
  • Understand and apply modern machine learning techniques for supervised, unsupervised, and reinforcement learning

Prerequisites by Topic
  • A fundamental understanding of structured programming languages
  • A fundamental understanding of data structures and algorithms
  • A fundamental understanding of probability and statistics

Course Topics
  • Problem solving, uninformed search
  • A* search and heuristic functions
  • Local search
  • Constraint satisfaction
  • Online search
  • Game playing
  • Logical agents, propositional logic
  • Forward chaining, backward chaining, knowledge agents
  • More knowledge-based agents
  • First order logic
  • First order inference
  • Knowledge representation
  • Acting under uncertainty, axioms of probability, inference using joint distributions
  • Bayes networks
  • Machine learning
  • Supervised learning: naive Bayes, decision trees, and neural networks
  • Unsupervised learning: clustering with K-Means, K-Medoids, and hierarchical agglomerative clustering
  • Collaborative filtering
  • Reinforcement learning
  • Data mining
  • Concept learning and inductive hypothesis

Laboratory Topics
  • History, defining intelligence, grand challenges, biologically inspired computing
  • Intelligent agents, uninformed search, informed search
  • Knowledge based agents
  • Machine learning

Coordinator
Dr. Jay Urbain



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