Description
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.
Credits: 4†
01:198:314.
Please note that courses for which a student has received a grade of D cannot be used to satisfy prerequisite requirements.
Semesters Offered:Fall
Topics: Search: Problem Spaces. Weak Methods, Game Trees
Knowledge Representation and Reasoning: Logic, Resolution Semantic Nets.
Frames
Planning
Machine Learning: Concept Learning, Connectionism
Natural Language: Grammars. Transition Networks
Vision
Expert Systems
Expected Work: Expected Work: Regular class assignments: 4 problem sets, 2 Lisp programming assignments
Exams: one hourly and a final exam
Department 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).
† - Can be taken for graduate credit.
