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Introduction to Artificial Intelligence: Search and Reasoning

The course will introduce important ideas and algorithms in search and reasoning, and demonstrate how they are used in practical AI applications. Topics include A* search, adversarial search, Monte Carlo tree search, reinforcement learning, constraint solving and optimization, propositional and first-order reasoning.

Learning Outcomes

By the end of the course students will be able to:

  • Demonstrate a fundamental understanding of artificial intelligence (AI) and its foundations
  • Apply basic principles of AI to problems that require search and reasoning
  • Demonstrate awareness and a fundamental understanding of various applications of search and reasoning across various AI applications
  • Apply classical search and A* algorithm
  • Develop Adversarial Search algorithm
  • Formulate Monte Carlo Tree Search method
  • Investigate Reinforcement Learning approaches
  • Design and develop constraint satisfaction solving problem
  • Develop Propositional and First-order Reasoning approach
  • Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal impact

Course typically offered: In-class during the Winter 20 academic quarter

Prerequisites: This course is aimed very broadly at undergraduates in mathematics, science, and engineering. Prerequisites are elementary probability, linear algebra, and calculus, as well as basic programming ability in some high-level language such as C, Java, Matlab, R, or Python.

Next steps: Upon completion, consider coursework in our specialized certificate in Machine Learning Methods to continue learning.

More Information: For more information about this course, please contact

Course Number: CSE-41326
Credit: 4.00 unit(s)