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Principles of Aritificial Intelligence

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To provide a comprehensive introduction to current research methods in artificial intelligence. The course is appropriate both for nonspecialists who wish to acquire a strong grounding in the engineering aspects of computing with real-world data, and as a prerequisite to more advanced courses in artificial intelligence.

Credits: 
3
Category: 
B
Prerequisite: 

An undergraduate course in artificial intelligence.

Topics: 

Modeling structure and uncertainty in real-world data and reasoning with these models, including perception, categorization and learning. Applications to text and web processing, computer vision, speech recognition and language processing, and user interfaces. Planning for risk and reward. Applications to expert systems, medical decision-making, robotics and design.

Expected Work: 

Midterm and final examinations and occasional readings, problem sets, and programming assignments.

Professor: 
Casimir Kulikowski
Vladimir Pavlovic
Louis Steinberg
Matthew Stone
Semester: 
Fall
Course Type: 
Graduate

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

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