Oct 07, 2022  
2020-2021 Undergraduate Academic Catalog 
2020-2021 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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CS 2400 - Introduction to Artificial Intelligence

2 lecture hours 2 lab hours 3 credits
Course Description
The objective of this course is to introduce the basic concepts of artificially intelligent systems. Topics covered include knowledge representation, problem solving using search, and the agent framework.  The role of AI in engineering and computing systems is presented, and students complete exercises that develop skills in applying AI tools and languages to real-world problems.  (prereq: CS 2852 , CS 2300 , and one of MA 2310  or MA 1830)  
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Understand what constitutes artificial intelligence and be able to identify such systems as well as their limitations
  • Solve problems, including game play problems, through decision trees and search
  • Apply propositional and first-order logic to planning problems
  • Train neural networks for classification
  • Solve problems using reinforcement Q-learning
  • Understand machine learning concepts

Prerequisites by Topic
  • Understand and apply complex data structures and algorithms
  • Use appropriate algorithms (and associated data structures) to design and build working software systems
  • Understand the use of recursion in problem solving
  • Predict the runtime and memory utilization of algorithms based on complexity analysis methods
  • An ability to construct Python solutions to programming problems
  • Understand and apply mathematical functions, relations, and sets as well as the associated operations
  • Ability to form logic proofs using symbolic propositional logic
  • Understand and apply symbolic predicate logic

Course Topics
  • Introduction to AI, Turing Test, learning
  • Decision trees, search, BFS, DFS
  • A*, heuristics
  • Game playing
  • Planning
  • Propositional and first-order logic
  • Neural networks
  • Genetic algorithms
  • Reinforcement Q-learning

Laboratory Topics
  • Dungeon crawling
  • A*
  • Game playing
  • STRIPS planning
  • Implementations of a simple game
  • Implementation of a neural network with an application to classification
  • Group presentations on additional AI topics

Dr. Robert Hasker

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