This course provides an introduction to methods and techniques used in artificial intelligence. Overarching themes will be search, logical reasoning, probabilistic reasoning, and learning. Application areas of artificial intelligence such as natural language, computer vision and robotics may be used to draw examples from, but are not the primary focus of this class.
At the end of this course, students should have a good understanding of the research questions and methods used in artificial intelligence, and should also be able to use this knowledge to implement some of these methods. Students who take this course for 4 hours credit should also be able to understand and evaluate original research papers in artificial intelligence that build on and go beyond the textbook material covered in class.
Russell and Norvig Artificial
Intelligence: A Modern Approach (3rd edition, 2010).
This is a required text. All assigned
readings are from this book, unless indicated otherwise. Make sure to
get the third (blue) edition of the textbook. The red or green editions
are outdated, and should not be used for this class.
Advanced undergraduates and graduates in computer science, electrical and computer engineering, and related fields. Programming experience is necessary for the assignments. We assume basic knowledge of discrete math (CS 173/MATH 213) and data structures (CS 225).