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Introduction to Artificial Intelligence*


To provide a broad introduction to Artificial Intelligence. The course will cover both fundamental concepts such as search and knowledge representation, as well as applied work in areas such as planning and vision. This course is intended for both students majoring in Computer Science as well as nonspecialists with the necessary background who wish to acquire a general familiarity with 
Artificial Intelligence.


This is an undergraduate course that can be taken for graduate credit.

Prerequisite : 
Introduction to Discrete Structures I


Please note that courses for which a student has received a grade of D cannot be used to satisfy prerequisite requirements.


Search: Problem Spaces. Weak Methods, Game Trees 
Knowledge Representation and Reasoning: Logic, Resolution Semantic Nets.
Machine Learning: Concept Learning, Connectionism
Natural Language: Grammars. Transition Networks
Expert Systems

Expected Work: 

Expected Work: Regular class assignments: 4 problem sets, 2 Lisp programming assignments

one hourly and a final exam
Learning Goals: 
Computer Science majors ...
  • will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
  • will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
  • will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).
Course Type: 
Undergraduate & Graduate

Check the University Schedule of Classes to see if this course is open.

Request an Special Permission Number here if the class is full.