Artificial Intelligence
4003-455

Roxanne Canosa, Ph.D.

Professor Canosa's General Policies

Course Description:

An introduction to theories and algorithms used to solve problems where solution spaces may be large and complex, and necessary information may be imprecise and/or missing, including approaches designed to mimic human intelligence. Topics include the history of artificial intelligence research, agents, search strategies, planning, logic, reasoning under uncertainty, machine learning, and applications such as computer vision, pattern recognition, robotics, and expert systems.

Course Outcomes:

• Students will describe the attributes of various search techniques and the situations in which they may be used.
  Evaluation: Homeworks and exams.
   
• Students will describe and apply various techniques for logic programming and machine learning.
  Evaluation: Homeworks and exams.
   
• Students will implement standard intelligent systems algorithms.
  Evaluation: Programming projects.
   
• Students will discuss the history and implications of intelligent systems research.
  Evaluation: Homeworks and exams.
   

Programming projects are required. Homework will be assigned, but will not be collected. Students are expected to complete the homework before the due date and bring the homework to class on the due date, when the solution will be provided in class. Students may occasionally be asked to provide their solution to a particular problem to the rest of the class.

Course Information:

Course Schedule: