CS 4881 - Artificial Intelligence

• 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

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

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

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